Developing a - Taylor's Education Group



Developing a Pilot Medical Academic Word List and Collocation List of Textbooks in EnglishAnis AshrafZadehFaculty of Social Sciences and Leisure Management, Taylor’s University, Malaysiaanis.ashrafzadeh@taylors.edu.myJean-Pierre PoulainTaylor’s Toulouse University Centre (TTUC), Taylor’s University, Malaysia poulain@univ-tlse2.frAbstractThis corpus-based lexical study presents the most frequently used medical academic vocabulary across a wide range of medical textbooks in addition to their frequent collocation/phraseological patterns. The Medical Academic Word List of Textbooks (MAWLOT) was compiled from the Medical Academic Corpus with 3.5 million running words in written medical textbooks by examining the range and frequency of words outside the first 2,000 most frequently occurring words of English (BNC/COCA2000) (Nation, 2012) and the Academic Word List (AWL) (Coxhead, 2000). The list contains 505 word families, which accounts for 11.27% of the vocabulary of the corpus of the study. Including most frequent highly specialized medical terms of Greek/Latin sources, the MWLOT encompasses various sub-technical and technical vocabularies. A list of the most frequently-occurring collocation/phraseological patterns (MACL) of the words was also retrieved to help ESL/EFL (English as a second/foreign language) medical students (as the target audience of the study) fulfil their vocabulary needs in reading medical textbooks in English. Keywords: corpus linguistics; academic word lists; medical academic word lists; English for medical purposes; EMP; collocation lists, medical academic collocation listIntroduction It has been generally accepted that lexical knowledge is a necessary factor for successful reading comprehension (McKoon & Ratcliff, 2016). Previous research have suggested that academic vocabulary knowledge might positively affect the students’ academic writing (Li & Pemberton, 1994), discipline-specific learning (Khani & Tazik, 2013), and academic achievements (Townsend et al., 2012). To increase the students’ lexical knowledge in reading academic texts, the exact words must be identified in order to ensure that they are worth being studied to facilitate the process of learning for students and teaching for instructors. Academic vocabulary has been defined by Farrell (1990, p. 11) as “formal words with a high frequency and/or wide range of occurrence across scientific disciplines, not usually found in basic general English courses; words with a high frequency across scientific disciplines”. A synonym for ‘academic’ is ‘sub-technical’ (Laufer & Nation, 1999), which is usually distinguished from more technical, i.e. subject-specific vocabulary that is part of a discipline or field of enquiry like biomechanics or film theory, and also from general service vocabulary (very high-frequency general English). In addition, academic vocabulary is a practical tool for students of college/university-level academic subjects like medicine, media studies or musicology. More generally, since academic vocabulary is the language of learning (Hyland & Tse, 2007), teaching it explicitly by means of an EAP course can be expected to affect academic communication skills and overall academic performance. Understanding the teacher's explanations, discussing them, reading to get more information in the area, and writing about what they have learned are the students' needs in an academic context. The needs not only affect L2 students, who are studying EAP courses in English-speaking countries, but also those who have to attend lectures, tutorials in L2, read and study textbooks in L2, and talk to professors in L2. Furthermore, in addition to the students’ needs to expand their vocabulary knowledge, some researchers believe that most EFL learners only learn the definitions of English words, so their passive vocabulary cannot be easily reconstructed into acceptable chunks or natural and meaningful sentences (Sarvari et al., 2016). Therefore, in a study Lewis (2000) considered the importance of using collocations in language teaching and emphasized on the role of word combination patterns in language and their use in teaching and learning a language. He counted then collocation as the quickest path to acquiring the elements of learning a word, such as syntactical, phonological, and form information. Lewis (2000) also mentioned that collocations provide a more practical approach to language teaching syllabus design. Therefore, the aim of the present study is to develop an academic word list of textbooks (MAWLOT) besides a medical collocation list (MACL) as important tools for academic success (Hyland & Tse, 2007) of medical academic English learners, especially, in the level of general practitioner (GPs).Literature ReviewIt is obvious that academic vocabulary is important for academic study because of its high frequency in academic texts (Nation, 2001). According to Nation (2001), students’ academic vocabulary knowledge can increase the familiarity with the unknown words in academic texts, which may result in better comprehension of academic English (Townsend et al., 2012). In addition, Hyland and Tse (2007) define ‘academic vocabulary’ as the vocabulary taught in schools, which is critical to understanding the concepts of the content. They also mention that in identifying academic vocabulary the notice should be paid to the degree of importance of the terms. That is, some vocabulary is used more frequently than other words in different academic contexts, or they are found more frequently in textbooks than in articles. Similarly, some vocabulary is used more often in medical textbooks than in science or engineering ones. Likewise, previous studies show that those words that occur frequently in students’ academic texts and are relevant to their needs identify and classify for pedagogical use (Nation, 2000), and have to be taught and learnt in the form of ‘word family’ (e.g., Nation, 2001). Word family is a word-counting unit that includes a headword, its inflections, and its derivations (Bauer & Nation, 1993), for example, approach (headword), approaches, approaching, and approached (inflections), approachable and unapproachable (derivations). Some researchers believe that by knowing a form of a word, students might be able to understand an unknown form when they encounter it for the first time (Schmitt, 2004; Webb, Sasao & Balance, 2017). The present study also adopts word as the ‘word family’, rather than the lemma because it may be a more useful unit of counting for receptive knowledge than the lemma (Nation, 2016). Furthermore, to obtain principles to select the most suitable words for inclusion in the Medical Academic Word List of Textbooks (MAWLOT) consideration takes first towards the different types of vocabulary. 2.1 Types of vocabularyNation (2001) divides vocabulary to four types: 1) high-frequency words, 2) academic/sub-technical words, 3) technical words, and 4) low-frequency words. ‘High-frequency’ words are a group of 2,000 word families (West, 1953; Nation, 2012) that is the essential basis for all use of language. A very large proportion of the running words in spoken and written texts, like question, work, low, right, metal, paper, and usually belongs to this group. Prepositions, conjunctions, and auxiliary verbs besides all other function words place in the high-frequency words category. ‘Academic/sub-technical’ words are a group of words that is not restricted to any specific domain, and occur frequently over a wide range of academic texts across disciplines. These words are common in academic texts; however, they are not technical terms in any particular domains either. Instead, they usually occur over a range of formal papers and academic texts such as university textbooks, field specific journals, reports, and manuals. Some examples of academic vocabulary are abstract, acknowledge, attitude, clarify, implicit, transfer, and volume. According to scholars (e.g., Nation & Webb, 2011; Nation & Waring, 1997), prioritizing learning high-frequency academic words can ensure that the focus of instruction is on the academic words that students most often encounter in their study. It also can avoid students’ exhausting themselves with massive burdens of vocabulary learning (Durrant, 2009).In contrast to academic/sub-technical words, the third group are ‘technical words’, which are the words that are particularly unique and are used only in one discipline like medicine, math, and science. These words are ‘discipline-specific’ words (domain-specific academic words or content-specific words) (Hiebert & Lubliner, 2008), and have just one meaning that understanding it is essential to build conceptual knowledge in the discipline. For example, the words phoneme, morpheme, lemma, and hapax legomena exclusively are used in applied linguistics, whereas the words anemia, hemoglobin, and gonadotropin are restricted to medicine. These words, which are narrowly distributed in a field like applied linguistics or medicine, are almost Latin/Greek words and will be useful for the study of specialist area-specific vocabulary. In the present study, the term ‘academic’ is used more broadly to refer to both general (medical academic words) and subject-specific vocabulary (medical technical words). Finally, ‘low-frequency’ words are so far the biggest group of words among the four groups of words and contain all of the words that are not included in the other three groups. The examples of these words, which typically occur in a very narrow range and at a low frequency, are proper nouns that rarely found in language use, and are found only once or twice in a text and seldom appearing in other texts. 2.2 Academic discipline-specific word listsAn academic word list is a group of high-frequency academic words viewed as most valuable and helpful for students’ academic study in English (Nation & Webb, 2011; Meara & Nation, 2013). Academic word lists are important for academic success when the included items in the list are selected according to certain criteria (Coxhead, 2000; Gardner & Davies, 2014). The purpose of compiling an academic list is to locate clearly the most commonly used academic vocabulary in a well-organized and efficient way (Coxhead, 2000; Gardner & Davies, 2014). One of the earliest endeavours in developing academic lists was Thorndike and Lorge’s (1944) study, who based their work on 18,000,000 running words, which they sifted through manually. The other one was the study of Campion and Elley (1971), which analysed 301,800 words from 19 academic disciplines in both textbooks and journals, and retrieved 500 lemmas. The following year, Praninskas (1972) published the American University Word list from a corpus of 272,466 words of 10 academic disciplines in 10 university-level textbooks. Lynn (1973) and Ghadessy (1979) listed the words that foreign students had difficulty with in their reading. Furthermore, Xue and Nation (1984) published the University Word List (UWL) consisting of about 800 words that included high frequency and wide-range words in academic texts. Finally, Nation (2012) published the BNC/COCA25000 as a group of 29 lists of words with 25 most frequent 1,000 word family lists and 4 additional lists of proper nouns, marginal words, transparent compounds, and abbreviations. BNC/COCA2000, which is also used in the present study, is a general high-frequency English vocabulary list containing 2,000 word families common in English texts, and composed of the first and second BNC/COCA 1,000 word family lists. In addition, Coxhead's (2000) Academic Word List (AWL) is another well-known academic word list. Coxhead retrieved the list from a corpus of about 3.5 million running words selected from different university textbooks and academic journals. The AWL consisted of 570 word families (about 10% of the corpus) in four main areas, i.e. natural sciences, arts, law and commerce. AWL has recently been the subject of some discussion in its usefulness for all academic domains (Lei & Liu, 2016). For example, Hyland and Tse (2007) and Ward (2009) mention that teachers and materials developers should guide students to apply a word list, which contains high frequency words, and academic and specialised/technical vocabulary in order to guide them through their academic studies. In 2013, Gardner and Davies developed a new Academic Vocabulary List (AVL) from a 120-million-word academic corpus. The AVL contains 3,015 lemma-headwords and 1,991 word families, and covers around 14% of the Corpus of Contemporary American English (COCA) academic sub-corpus and the British National Corpus (BNC) academic sub-corpus. Recently, researchers have focused more on a single discipline to develop academic vocabulary lists. Some resulting lists are the Engineering Academic Word List (Mudraya, 2006), the engineering word list of textbooks (Hsu, 2014), the academic word list of agriculture (Martinez et al., 2009), the academic word list of finance (Li & Qian, 2010), and the academic word list of applied linguistics (Vongpumivitch, Huang & Chang, 2009). However, despite an abundance of vocabulary research in different disciplines, there are comparatively few studies, other than those of Chen and Ge (2007), Wang, Liang, and Ge (2008), Hsu (2013), and Lei and Liu (2016), exclusively interested in medical academic vocabulary. Chen and Ge (2007) developed a corpus of medical research articles with 190,425 running words (tokens) taken from 50 medical research articles, and investigated the text coverage of the Academic Word List (AWL) (Coxhead, 2000) across the corpus. They found that the AWL, however useful, but was not able to demonstrate the most frequently used medical academic vocabulary words in medical research articles. Wang, Liang, and Ge (2008) established the Medical Academic Word List (MAWL) from a corpus of 1,093,011 running words with 288 written texts of medical research articles. MAWL contained 632 word families. In addition, the study of Hsu (2013) established a word list (MWL) based on the vocabulary needs of Taiwanese undergraduate medical students. The corpus to extract the list consisted of about 15 million running words from e-book databases. MWL contained 595 word families by signifying the words based on the different degrees of technicality. The study of Lei and Liu (2016) also developed a list (MAVL) of 819 lemmas. The list retrieved from a corpus of 2.7 million running words of medical journal articles besides 3.5 million running words from medical English textbooks. Finally, the present study endeavours to respond to the need of students in the specific discipline of medicine by developing a list from written textbooks because the main users of medical vocabulary lists are medical students who “have to read medical textbooks in their study” (Lei & Liu, 2016, p.46). 2.3 Collocation and its role in second/foreign language learningThe second aim of the present study is to develop a list of the most frequently used collocation/phraseological patterns of the academic words included in medical textbooks. Developing vocabulary lists and phraseology lists is one of the most important applications of corpus-based research over the past 70 years (Miller & Biber, 2015). The list may help ESL medical students understand and manage lexis as well as to communicate ideas more effectively. It may also consider as a basic resource for learners and teachers in medical English education. 2.3.1 Defining collocationThere is a reasonable degree of agreement among researchers about defining a collocation as the combination of words (McKenny, 2006); however, they believe that different types of text reflect different collocation patterns as well. For example, Partington (1998, p. 17) argues, “Collocational normality is dependent on genre, register and style”. Similarly, Lewis (2000, p. 186) mentions, “different kinds of text have radically different collocational profiles”. For example, different meanings of heart in romantic and medical tests are indicated clearly by different collocations (Heartthrob- romantic novel; heart failure – medical text) (Murison-Bowie, 1996, p. 194). In the viewpoint of Bennett (2010), collocation is a prominent way in studying phraseology to find the statistical tendency of words to co-occur. Furthermore, Lehecka (2015) counts collocation as a fundamental concept in usage-based studies in many linguistic fields, most notably lexical syntax and semantics. Lehecka mentions that studying collocations in large electronic corpora allows for statistical analyses of the co-occurrence patterns of linguistic items. The present study adopts the definition of Lehecka (2015) to analyse and find the linguistic patterns to include in MACL. 2.3.2 The role of collocation in language learningSome ELT (English language teaching) researchers (e.g., Bahns, 1993; Hill, 1999; Lewis, 1997; Lewis, 2000; Ying & Jingyi, 2014) have previously studied the benefits of teaching collocations for second/foreign language learning. The researchers mention knowing the definition of a word is often not enough (Nagy, 1988), but it involves knowing its collocations as well (Ying & Jingyi, 2014). They believe that, however, students may know the meaning of a word but mastering the use of a certain word needs to know the collocation of the word. For example, the words journey and trip have similar dictionary definitions but the acceptable form in combination with word business is business trip not business journey. In addition, “the way words combine in collocations is fundamental to all language use” (Hill (2002, p. 24). For example, we use the phrase of poisonous snake but not the toxic snake. This ‘collocational restrictions’, i.e. knowing which words collocate and which do not, is an important reason for teaching collocations to learners. The important role of ‘fluency’ for language learners is the other reason to teach collocations, which is supported by the theories in cognitive psychology (Ying & Jingyi, 2014) such as the Multiple Resource Theory (MRT) of Wickens (1984). The Wickens’ theory mentions that in facing a difficult task, the human brain refers to several different resources?to?tap simultaneously. Having?automated human?resources?processes, which are ready to use by the brain without much processing, enables the brain to pay more attention on the more difficult components of the task. According to Raupach (1984), in performing language production tasks, collocations or ‘language chunks’ function as automatized resources that allow for fluency in production and faster processing of language skills. Having a huge stock of ready-made chunks available for use by native speakers enable them to express their thoughts rapidly and fluently. Finally, the researchers mention that teaching collocations allows students to improve their level of the target language to the level of advanced speakers of the language (Ying & Jingyi, 2014). According to Lewis (2000, p. 177), advanced students have “a sufficiently large and significant phrasal mental lexicon” that enable them to express themselves effectively. Having this collocational competence enables students to create shorter utterances because they know “the collocations which express precisely what they want to say” (Hill, 2000, p. 49). A comparison of the following two sentences helps illustrate the point. a) Do you know her departure time? b) Do you know what exact time her plane will leave the airport? Both sentences are grammatically correct and comprehensible, but sentence b is clearly not as concise and effective as sentence a. An example of a general collocation list is the Academic Collocation List (ACL) (Ackerman & Chen, 2013), which compiled from the written curricular component of the Pearson International Corpus of Academic English (PICAE) comprising over 25 million words. ACL includes 2,469 most frequent and pedagogically relevant lexical collocations in written academic English. MethodsThe purpose of this pilot study is to develop a medical academic word list of textbooks (MAWLOT) besides a collocation list (MACL) of the words included in the list. Three research questions frame developing the MAWLOT and the MACL.1. What words are included in the MAWLOT? 2. What percentage of the running words in the medical academic corpus does the MAWLOT cover? 3. Do the included words in MAWLOT reveal particular patterns in the corpus of medical textbooks?3.1 The Medical Academic CorpusThe corpus for the study compiled from 62 medical textbooks across 31 medical subject areas. The subject areas, according to the discipline of Medicine and Dentistry of ScienceDirect, involved: 1) anesthesiology, 2) allergology/immunology, 3) alternative/complementary medicine, 4) cardiology, 5) dermatology, 6) dentistry, 7) endocrinology/ metabolism, 8) emergency medicine, 9) forensic medicine, 10) gastroenterology, 11) haematology, 12) herpetology, 13) health informatics, 14) urology, 15) infectious diseases, 16) intensive care medicine 17) neurology, 18) nephrology, 19) obstetrics/ gynaecology, 20) oncology, 21) ophthalmology, 22) orthopaedics/rehabilitation, 23) otorhinolaryngology, 24) perinatology/paediatrics, 25) psychiatry, 26) pathology, 27) pulmonary/respiratory medicine, 28) public health, 29) radiology, 30) surgery, and 31) transplantation. In addition, the target population was the published English medical textbooks printed as the last references for medical students at the level of general practitioners (GPs) in 2018. The reference textbooks (at least two textbooks being selected for each sub-discipline) were retrieved from the English-speaking medical universities such as Harvard, UCLA, University of Washington, Dalhousie University, University of Kansas, University of Dundee (UK), and University of Toronto (e.g., Nelson Essentials of Pediatrics, 2018). The textbooks covered all topics and subjects that medical students will study in their foundation curriculum (see Appendix D for the list of textbooks). The rationale for using a corpus of medical textbooks is that the target users of the study are medical students (GPs) who generally have to read medical textbooks as the main sources in their study, while the users of research articles, for example, are mostly professionals (Lei & Liu, 2016) at advanced level (Hsu, 2013). According to Lei and Liu (2016), although research articles are an important academic genre, the importance of including textbooks in any corpus used to develop a word list for students is crucial. In addition, English-language textbooks are widely used in many universities and medical schools across the world, not only by students but also by lecturers and language instructors to determine what vocabulary to teach in their English for Medical Purposes (EMP) courses. Therefore, it is vital to include medical textbooks to the corpus of the study as a source to ensure that the selected words to include in the MAWLOT are indeed those the medical students will encounter in their study. Furthermore, developing the corpus required to collect all texts in the electronic form, i.e. text files (~.txt) by removing all photos, tables and figures that were not readable by computer. All computer programs (e.g., the Word program) can do this stage to standardize the words. The program software of Compleat Lexical Tutor (CLT) did the normalization of words by reading all inflections and derivations of words as well. Finally, the Medical Academic Corpus, as a single combined file for analysis on the CLT, compiled from 3.5 million tokens (running words) (excluding tables, notes and references) with 2,659 different types from 954 texts of 12,500 pages of medical English textbooks. Every sub-corpus contained approximately equal number of running words, including almost 113, 000 tokens. 3.2 InstrumentsThe Medical Academic Corpus was run by the Compleat Lexical Tutor (CLT) version 8 to calculate lexical coverage and frequencies. CLT (Cobb, 2011) is an online software program, which is available at the URL address . The software is an adapted form of the RANGE program (Nation & Heatley, 2005), and includes three main sections: Tutorial, Teachers, and Research. The third section, i.e. Research, is the most relevant part to the study by including ‘Vocab Profile - the Web Vocabulary Profilers’, which can be used with any text. ‘Vocab Profile’ classifies words into four categories by frequency, i.e. 1) the first most frequent 1,000 words of the BNC/COCA2000 (Nation, 2012), 2) the second most frequent thousand words, i.e. words 1,001 to 2,000 of the BNC/COCA2000 (Nation, 2012), 3) academic word list (AWL), and 4) the remaining items which are not found in the BNC/COCA2000 and in the AWL, i.e. ‘Off list’. In the CLT, ‘Off list’ acts as ‘Not in the lists’ in the RANGE program. Farjami (2014) has already used the CLT to explore the frequency of words in the abstracts of applied linguistics journal articles. Another instrument used in the study was the AntConc’s software version 3.4.4 to develop the MACL. AntConc (Antony, 2015) is a freeware application, which runs on both Windows and Linux systems. It is also a user-friendly software, and available at the URL address . Researchers in the corpus-based studies (e.g., Noguchi, 2004; Farjami, 2016) used AntConc extensively, and described it extremely effective (Noguchi, 2004). In developing the MACL of the present study, the AntConc’s software was also more user-friendly than the CLT. 3.4 Word selectionFour key principles guided us to select the words for inclusion in the MAWLOT. The principles were specialised occurrence, range, frequency, and dispersion.Specialised occurrence: The word families included in the MAWLOT had to be outside the words of the 1st and 2nd BNC/COCA 1,000-word lists, which are often viewed as general high-frequency words (Nation, 2012), and the AWL (Coxhead, 2000). The rationale for excluding words of the BNC/COCA2000 (Nation, 2012) and the AWL (Coxhead, 2000) from the MAWLOT is to indicate that the list is specifically for medical students. Some researchers have already used this methodological approach, i.e. excluding the general high frequency words such as the GSL and the BNC/COCA2000, and the AWL to develop a specialised word list. For example, Fraser in 2007 compiled the Pharmacology Word List (PWL) based on a corpus of pharmacy research articles along with the GSL and the AWL. The resulting lexical coverage was 88% of the Fraser's corpus. Again, in 2009, Fraser developed a PWL consisting of only core pharmacological words (2,000 words) by excluding the GSL and the AWL. The resulting lexical coverage was higher (89%) than the first PWL in his corpus. In the present study, it is obvious that the BNC/COCA2000 with 2,000 word families common in English texts (Nation, 2012), and the AWL (Coxhead, 2000) with 570 word families “for students of commerce, arts, law, and science” (Coxhead, 2000, p. 223) do not contain predominantly medical academic words for medical students as the target learners of the study. 2. Range: The word families included in the MAWLOT had to occur at least in 16 or more of the 31 subject areas (more than half of the sub-corpora) in the Medical Academic Corpus. This measure (range) serves to ensure that the selected words occur in a wide range of corpus. To avoid bias by topic-related words in development of the AWL and the MAWL, Coxhead (2000) and Wang, Liang, and Ge (2008) also specified that the words should appear in at least half of the sub corpora. 3. Frequency: Members of a word family, taken together, had to occur at least 100 times in the Medical Academic Corpus. This decision was made because “studying the data of the Brown Corpus (Francis & Kucera, 1979) shows that a corpus of around 3.5 million words would be needed to identify 100 occurrences of a word family” (Coxhead, 2000, p. 217). Coxhead (2000) and Wang, Liang, and Ge (2008) used the same threshold in developing the AWL and the MAWL. In addition, by adopting a cutting point of occurring less than 100 times, the developed wordlist would contain more vocabulary than what medical students need to comprehend texts (Hsu, 2013). Dispersion: The words must occur at least 3 times or more in each of the 31 sub corpora of the Medical Academic Corpus for inclusion in the word list. This criterion has adopted as an additional criterion alongside specialised occurrence, range and frequency in the studies of Coxhead (2000), Coxhead and Hirsh (2007), Hirsh (2010), and Lei and Liu (2016) as well. Dispersion is “a measure of how evenly distributed occurrences of a word are across equally sized sections of a corpus” (Leech, Rayson, & Wilson, 2001, p.18). In the present study, we also used the measure of dispersion to guarantee that a selected word appeared evenly in the corpora.4. Data analysisThe Medical Academic Corpus was analysed through the Compleat Lexical Tutor (CLT) to extract the MAWLOT’s words to response the Research Question 1. To recognise the words, we used the Web VP Classic v.4 (available at ) from the section of the ‘VocabProfilers’ (VP) of the Compleat Lexical Tutor (CLT). The section is able to break texts down by word frequencies in the language at large as opposed to in the text itself. The Vocabprofilers are based on the Laufer and Nation's Lexical Frequency Profiler (1995) 4-way sorter, which divide the words of texts into the first and second thousand levels, academic words, and the remainder or 'off list’. In addition, to check and establish the viability and representativeness of the MAWLOT, the obtained data was analysed in the section of Text Lex Compare (TLC) v.3. TLC is available in the Research part of the CLT (), and is able to subtract one text from another, one list from another and a list from a set of lists or texts. TLC compares texts in 10 different ways according to the user’s aim as well. It is user-friendly, and does not have any limitation on the text size to upload. The output dedicates two important lexical items: Tokens Recycling Index (TRI) and Families Recycling Index (FRI). According to the program, TRI is normally the most interesting measure of text comprehensibility, which achieves the division of the repeated tokens into the tokens in the new text. The different amounts of TRI in different sub corpora showed differences in occurrence of the words included in the MAWLOT across the sub corpora, and consequently the total coverage of the corpus beyond the BNC/COCA2000 (Nation, 2012) and the AWL (Coxhead, 2000). Furthermore, proper nouns (e.g., Africa, America, and Lancet), which are generally considered less important for language learners (Nation, 2004, Schmitt, 2004), and abbreviations (e.g., MD) removed from the list. There were also some removed abbreviations/acronyms, which appeared frequently in medical textbooks, and should not be ignored (Hsu, 2013) (see Table 1).NumberWordFrequencyCoverage (%)Range1234CNSDNAFSH HIV 1875221112630.0050.0150.0030.0073131183156IQ MRI 1611430.0050.00425307PH 1430.00426 Table 1. The most frequent abbreviations/acronyms of medical textbooksCompound words (with/without hyphens) were also added to the list by replacing the hyphens with space. Furthermore, as said in the literature review, in this study we use the term ‘academic’ more broadly to refer to both general and subject-specific vocabulary. Therefore, the discipline-specific words (mostly Latin/Greek words), which met the criteria for inclusion in the MAWLOT and appeared in in the category of ‘off list’, were added to the list (with bold font). The rational for including the discipline-specific words in the list is that the medical textbooks are full of the esoteric medical terms of Greek/Latin sources, and inevitably nonnative speakers/medical novices will encounter this type of vocabulary when reading medical texts (Hsu, 2013). Finally, the modified list (MAWLOT) developed by including 505 word families. In addition, since this pilot study also aimed to develop a Medical Academic Collocation List (MACL), twenty top frequently-occurring words of the MAWLOT (see Table 2) were selected for finding their particular collocation/phraseological patterns to help students in studying medical texts. NumberWordFrequencyCoverage (%)Range1patient89430.25312disorder8457 0.24313diagnosis3765 0.10314therapy3526 0.10315syndrome3519 0.10316fetal3498 0.09317cell3428 0.09318clinic2107 0.06319acute2014 0.053110infant2000 0.053111drug1979 0.053112cancer1873 0.053113adolescent1872 0.053114chronic1863 0.053115dose1819 0.053116abuse1691 0.043117maternal1543 0.043118psychiatric1462 0.043119acid1451 0.043120protein1396 0.0431Table 2. The twenty top frequent words of the medical academic word list of textbooks According to Lehecka (2015, p. 2), “If the observed number of co-occurrences of W1 and W2 is larger than what can be ascribed to chance, then W2 is a statistically significant collocate of W1”. In this regard, the corpus of the study was analysed by the AntConc software to find the most frequent and pedagogically relevant patterns of the words to apply by EAP (EMP) teachers and medical students. The analysis was based on?the following steps: 1) analysing the sub corpora by the Word List part of the AntConc software, 2) identifying the most frequent patterns of the words by using N-Grams part of the software. For example, analysing the word patient (N) by N-Grams reveals that the most frequent part of speech which results immediately to the right of the N are with (N with) and in (N in). The symbol N is in uppercase as we are focusing on a noun (Hunston & Francis, 2000), 3) finally, selecting at random a number of concordance lines, which have been sorted into alphabetical order (Hunston & Francis, 2000). Following Hunston and Francis (2000), fifty concordances were selected from all retrieved concordance lines that contained the examined word to investigate the patterns. Depending on the word, the concordance lines may be right-sorted or left-sorted. In the case of a verb, noun, and an adjective it is better to sort them to the right (Hunston & Francis, 2000) to reveal their complementation patterns (e.g., the diagnosis of acute MI; in a patient with a trichobezoar). The selected concordance lines of this study are also right-sorted. In addition, the lines were checked against the further sets of the 50 randomly selected concordance lines to assure that the collocations would not change with a different selection. Finally, due to the Kjellmer’s (1984) suggestion, in a corpus such as the Brown corpus with one million words, a string of words must happen more than once in order to be counted as a collocation. Therefore, in the present study with 3.5 million running words, the collocations that have occurred at least 3 - 4 times in the corpus were selected to include in the MACL. 5. Results5.1 Research Question 1: What words are included in the MAWLOT? The findings of Research Question 1 yielded the Medical Academic Word List of Textbooks (MAWLOT) with 505 words. Some of the most frequent word families in the MAWLOT are patient (F=8943, R=31), disorder (F=8457, R= 31), therapy (F= 3526, F= 31), syndrome (F=3519, R=31), and fetal (F=3498, R=31). Some of the least frequent words are neuron (F=100, R=25), tubular (F=100, R=31), carcinoid (F=100, R=25), and oxidation (F=100, R=31), (F=Frequency, R= Range). Appendix A shows the included words of the MAWLOT with their frequency and range.57791353475989005.2. Research Question 2: What percentage of the running words in the medical academic corpus does the MAWLOT cover? The results show that the total coverage of Medical Academic Corpus, beyond the BNC/COCA2000 (Nation, 2012) and the AWL (Coxhead, 2000), is 11.27%. This amount is close to the total coverage of the MAWL of vocabulary of the corpus in the study of Wang, Liang, and Gi (2008) (12.24%) based on the medical research articles. The coverage was different among sub corpora, for example, from 9.23% for the sub corpora of psychiatry and mental health to 10.74% for obstetrics and gynaecology, and women’s health. The MAWLOT’s coverage was slightly higher (between 12 to 12.14% ) for the sub corpora of anaesthesiology and pain medicine, cardiology and cardiovascular medicine, clinical neurology, nephrology, critical care and intensive care, dermatology, emergency medicine, endocrinology, diabetes and metabolism, gastroenterology, haematology, immunology, allergology and rheumatology, infectious diseases, oncology, ophthalmology, pulmonary and respiratory medicine, and urology. For the sub corpora of orthopaedics, sport medicine, rehabilitation, otorhinolaryngology, and facial plastic surgery the coverage of the MAWLOT was from 11% to 11.78%. For the sub corpora of perinatology, paediatrics, and child health the coverage of the list was between 12 and 12.51%. 5.3. Research Question 3: Do the included words in MAWLOT reveal particular patterns in the corpus of medical textbooks?The results of this pilot study revealed 133 different types and frequent collocation/phraseological patterns of the 20 top words of the medical academic word list of textbooks (MAWLOT) (see Appendix B). The word diagnosis (F= 3765, R=31) revealed the most different types of collocations (14 different collocation types), and the word dose (F= 1819, R= 31) revealed the least different types of collocations (just one collocation type) in the list. In addition, the list included the most frequent medical technical words with Greco-Latin origins. Four top words (out of 46 technical words included in the list) were selected for considering their particular collocation/phraseological patters. These words are cesarean (F= 529, R= 25), esophageal (F=512, R=31), etiology (F=478, R=31), and anemia (F=385, R=31). The words revealed 22 different types of collocation/phraseological patterns. The words cesarean and esophageal revealed 16 different types of collocations (each 8 different types), and etiology and anemia revealed six different types of collocation/phraseological patterns (each 3 different types) (see Appendix C). The developed Medical Academic Collocation List (MACL), like other subject-specific collocation lists, may be useful in helping students to practice the target vocabulary relevant for their academic studies (Lessard- Clouston, 2013). Discussion This pilot study developed a list of 505 medical academic words from core academic words, i.e. MAWLOT besides a list (133 different types) of the most frequently used collocation/phraseological patterns (MACL) of the 20 top frequent words included in the MAWLOT to help medical students (GPs) in their vocabulary needs. The total coverage of the MAWLOT is 11.27%, close to the total coverage of the MAWL (12.24%) of vocabulary of the corpus of research articles. The findings also reveal that of the 505 word families in the list, 462 word families (91.5%) in the MAWLOT occur in 25 or more of the sub disciplines of the Medical Academic Corpus of the study. In addition, including the most frequent medical technical words in the list may provide students with a chance to expand their vocabulary for reading medical texts. One of the most important applications of corpus-based research over the past 7 decades has been developing vocabulary lists and phraseology lists, which contain words or lexical phrases found in a discourse domain (Miller & Biber, 2015). The lists also include frequency information for the use of particular words “to help focus our vocabulary teaching” (Lessard-Clouston, 2016, p. 6). From the first word lists compiled for academic purposes (e.g., the American University List (Praninskas, 1972)) to the recent ones (e.g., Academic Vocabulary List (AVL) (Gardner & Davies, 2013)), the frequency of words has been considered as a crucial variable to develop word lists for academic purposes (Aitchison, 1987). Some researchers even define academic vocabulary based on their frequency. For example, academic vocabulary is defined as the vocabulary that is more frequent in academic texts than in general texts (Hagen et al., 2016). In addition, academic core words are “those that appear in the vast majority of the various academic disciplines” (Gardner & Davies, 2013, p. 8). Similarly, studies on the effect of word frequency show that lists of high-frequency words are better recalled than the lists of low-frequency words (Miller & Roodenrys, 2012). A word list by frequency "provides a rational basis for making sure that learners get the best return for their vocabulary learning effort" (Nation & Waring, 1997, p. 17). Furthermore, the utility of frequent words and compilation of specialised word lists in language teaching have been considered by many researchers. For example, Jurafsky (1996, cited in Baker, 2012) mentions that frequent words are recognized faster than infrequent words, and learners use them more than infrequent words (Tono, 2002). Likewise, Chanasattru and Tangkiengsirisin (2016, p. 45) mention that teachers in Thailand cannot decide which vocabulary is useful to prepare ESL/EFL students to read and write, because “teachers do not know which words frequently appear and are truly representative of the vocabulary in specific fields”.Considering existing word lists, however, it reveals that for most learners in specific fields the ideal list does not exist (Kerr, 2015). Either it will have to be created, which requires a significant amount of time and expertise (Timmis, 2015), or available best fit will have to suffice, many studies (including the present study) have been done to develop frequency wordlists to apply in language teaching. For example, the Academic Word List (AWL) (Coxhead, 2000) developed from the Academic Corpus with 3,513,330 running words (tokens) of written academic text by examining the frequency, range, and dispersion of words outside the first 2,000 most frequent words in English, i.e. the General Service List (GSL) (West, 1953). The corpus to develop the AWL included 3,110 different types of words consisted of disciplines of arts, commerce, law, and science. The resulting list (AWL) contained 570 word families with the coverage of about 10% of the vocabulary of the Academic Corpus (Coxhead, 2000). Furthermore, to establish the medical academic word list (MAWL) of research articles (RAs), Wang, Liang, and Ge (2008) included 1,093,011 running words from 1800 journals in a corpus consisting of different sub disciplines of medicine. The resulting word list (MAWL) contained 623 word families with coverage of 12.24%. Finally, the findings of the present study show that the study has created a university word list of the most frequent words of medicine outside the BNC/COCA2000 (Nation, 2012) (i.e. non-academic words) and the AWL (i.e. academic words) to represent a medical academic word list that medical students are exposed to and meet their specific vocabulary needs. The Medical Academic Word List of Textbooks (MAWLOT) compiled from a corpus with 3,513,378 running words with 2,659 different types, and a coverage of 11.27% to expand the vocabulary knowledge of general practitioners (GPs). In addition, the developed list of the particular collocation/phraseological patterns (MACL) of the top words of the MAWLOT may help students “to produce language that is phraseologically similar to that of native speakers” (Hunston & Francis, 2000, p. 10). The important role of collocations for second language learners have been studied by many researchers. For example, Smith (2005) mentions that collocations must be included in curriculum and Durrant (2009) counts collocations as the most significant component of turning passive words into active ones. According to Shabani (2016, p.21), “If L2 words are not properly taught, students soon will face communicational problems”. Similarly, Woolard (2000) believes that collocation is an important category of lexical patterning, and it is becoming a recognized unit of description in language teaching courses and materials. Furthermore, “collocation in particular has become a fundamental concept in usage-based studies in many linguistic fields, most notably lexical syntax and semantics” (Lehecka, 2015, p.2). Some researchers (e.g., Xue & Nation, 1984; Hoshino, 2010) suggest that teachers have to help students to understand collocations and their relation to the particular items of the word lists that they use, therefore, teachers have to teach word lists and their collocations, so that students can see the relationships between words (Lessard- Clouston, 2013).Therefore, the words included in the MAWLOT in addition to their collocations are meant to enhance medical students’ vocabulary knowledge of academic criteria while also expanding their conversational abilities within an English-speaking context. Hence, a truly effective strategy for learning and comprehension the MAWLOT’s words is simply to practice the words by employing some general strategies and specific activities to teach the MAWLOT. Some general strategies to teach the MAWLOT are:Put the words into context, either by reading or by listening the text that has a clear topic. Memorizing the MAWLOT’s words is difficult, boring, and usually ineffective. Therefore, it is far better students learn words in context by using strategies and activities, and by remembering that the MAWLOT’s words are not listed by theme but by frequency, then the words are unrelated dramatically.Use the medical academic collocation list (MACL) to teach the words of the MAWLOT. For example, teach the word FETAL with its collocations, i.e. fetal heart block, fetal heart rate, fetal alcohol syndrome, with cells of fetal origin, fetal heart rate abnormalities, fetal heart rate acceleration, during fetal development, and during fetal life. Teach all of the words in the word families. MAWLOT has 505 headwords, and each headword has word family. Techers need to make sure that they teach all the headwords as well as the words in the family. Work with a part of the list at a time, and then recycle the words. For example, teachers can introduce a few words in one week, revisit them next week, and review the following week. Provide students with the opportunity to use the words in real contexts to help them not only to be successful now, but also to stay successful in the future!Teachers can also use some specific activities to teach the MAWLOT. For example:Students would find all the members of the word families for the assigned words from the MAWLOT. They define the words and write example sentences for each.Students can complete rote learning (a?memorization?technique based on repetition) and controlled practice activities for learning the MAWLOT’s words by, for example, definition matching, crossword puzzles, filling the blank, categorizing, and multiple-choice questions. Students would identify word class, synonyms and antonyms for words of the MAWLOT. They would write definitions for each word along with the example sentences.Students complete the fill-in-the-blank activity by selecting the correct word family member of the headword. For example, they may choose ‘aorta’ or ‘aortic’ in “She explained that an?aortic?dissection occurs when the layers of your?aorta?separate, and that leaves the?aorta?subject to rupture”. Students would read a text, and identify the words of the MAWLOT in the reading text. They would define each word they find based on the context clues from the text. Students would listen to a lecture, and identify the words of the MAWLOT. They will define each word based on the information from the lecture. Students would read a piece of the academic writing that they have completed, and underline the assigned words of the MAWLOT. They will then look again at the piece of writing and see if they can add more words from the assigned words. Students analyze the presentation they have developed, and identify the MAWLOT’s words they have used in the presentation. They will continue to work on the presentation by adding more words from the MAWLOT in the proper places of the presentation. Students can also complete a conversation activity by using a designated number of words from the MAWLOT. To encourage students, teachers can make a competition among students by placing them into teams, and award them based on the number of the MAWLOT’s words they have used in their conversation. Finally, this pilot study is based on a single medical discourse genre, i.e. medical textbooks, and does not consider other disciplines like spoken medical academic English. Therefore, for future studies researchers can recheck the MAWLOT in larger corpora or in other genres of medicine such as spoken medical academic English to investigate if the MAWLOT accounts for spoken medical academic English, or spoken medical academic English needs a separate medical academic word list. It would also be useful for future research to develop various specialist area specific medical vocabulary lists to meet medical specialists’ needs in L2 medical learners.ReferencesAckermann, K., & Chen, Y. H. (2013). Developing the Academic Collocation List (ACL): A corpus-driven and expert-judged approach.?Journal of English for Academic Purposes,?12 (4), 235-247.Aitchison, J. (1987). Words in the mind: An introduction to the mental lexicon. Oxford: Blackwell.Antony, L. (2015). AntConc (Version 3.4. 4) [Computer Software].?Tokyo, Japan: Waseda University. Available from laurenceanthony. net/software/antconc.Baker, p. (2012). Contemporary corpus linguistics. London/New York: Continuum.Bahns, J. (1993). Lexical collocations – a contrastive view. ELT Journal, 47(1), 56-63.Bauer, L., & Nation, P. (1993).Word Families. International Journal of Lexicography, 6 (4), 253-279.Bennett,?G.?(2010). Using Corpora in the Language Learning Classroom. Michigan: Michigan University Press.Campion, M. E., & Elley, W. B. (1971). An academic vocabulary list. Wellington: New Zealand Council for Educational Research.Chen, Q., & Ge, G. C. (2007). A Corpus-based lexical study on frequency and distribution of Coxhead’s AWL word families in medical research articles. English for Specific Purposes, 26, 502-514.Chanasattru, S. & Tangkiengsirisin, S. (2016). Developing of a High Frequency Word List in Social Sciences. Journal of English Studies, 11, 41-87.Cobb, T. (2011). Compleat Lexical Tutor (Version 8).Coxhead, A. (2000). A new academic word list. TESOL Quarterly, 32 (2), 213-238.Coxhead, A. & Hirsh, D. (2007). A pilot science-specific word list, Revue fran?aise de linguistique appliquée, 12, 65-78.Coxhead, A., & Nation. I. S. P. (2001). The specialized vocabulary of English for academic purposes. In J. Flowerdew & M. Peacock (Eds.). Research perspectives on English for academic purposes (pp. 252-267). Cambridge: Cambridge University Press.Durrant, P. (2009). Investigating the viability of a collocation list for students of English for Academic Purposes. English for Specific Purposes, 28 (3).Farrell, P. (1990). Vocabulary in ESP: A lexical analysis of the English of electronics and a study of semi-technical vocabulary. CLCS Occasional Paper, No. 25.Farjami, H. (2014). Key lexical chunks in applied linguistics article abstracts. Journal of Teaching Language Skills,?6 (3), 51-73.Francis, W. N., & Kucera, H. (1979). The brown corpus: A standard corpus of present-day edited American English.?Providence, RI: Department of Linguistics, Brown University [producer and distributor].Fraser, S. (2007). Providing ESP learners with the vocabulary they need: Corpora and the creation of specialized word lists. Hiroshima Studies in Language and Language Education, 10, 127-143.Fraser, S. (2009). Breaking down the divisions between general, academic and technical vocabulary: The establishment of a single, discipline-based word list for ESP learners. Hiroshima Studies in Language and Language Education, 12, 151–167.Gardner, D., & Davies, M. (2013). A new academic vocabulary list.?Applied Linguistics,?35 (3), 305-327.Ghadessy, M. (1979). Frequency counts, word lists, and materials preparation: A new approach. English Teaching Forum, 17 (1), 24-27.Hagen, K. Johannessen, J. B. & Saidi, A. (2016). Constructing a Norwegian Academic Wordlist. Tenth international conference on language resources and evaluation of LREC Proceedings, 1457-1462.Hiebert, E. H., & Lubliner, S. (2008). The nature, learning, and instruction of general academic vocabulary. In A.E. Farstrup & S. J. Samuals (Eds.). What research has to say about vocabulary instruction, (pp.106-129). Newark: International Reading Association.Hill, J. (2002). Revising priorities: from grammatical failure to collocational success. In Michael Lewis (Ed.) Teaching collocation: Further developments in the lexical approach (pp. 47-69). Hove, England: Language Teaching Publications.Hirsh, D. (2010). Academic Vocabulary in Context. Bern: Peter Lang Publishing.Hoshino, Y. (2010). The categorical facilitation effects on L2 vocabulary learning in a classroom setting.?RELC Journal,?41 (3), 301-312.Hsu, W. (2013). Bridging the vocabulary gap for EFL medical undergraduates: The establishment of a medical word list. Language Teaching Research, 17(4), 454–484.Hsu, W. (2014). Measuring the vocabulary load of engineering textbooks for EFL undergraduates. English for Specific Purposes, 33, 54-65.Hunston, S., & Francis, G. (2000). Pattern Grammar: A corpus-driven approach to the lexical grammar of English. John Benjamins: Amsterdam/Philadelphia.Hyland, K., & Tse, P. (2007). Is there an academic vocabulary? TESOL Quarterly, 41, 235-253. Kerr, P. (2015). Adaptive learning. ELT Journal, 70 (1), 88-93.Khani, R., & Tazik, K. (2013). Towards the development of an academic word list for applied linguistics research articles.?RELC Journal,?44 (2), 209-232.Kjellmer, G. (1984). Some thoughts on collocational distinctiveness. In J. Aarts &W. Meijs (Eds.).Corpus Linguistics (pp.163-171). Amsterdam: Rodopi.Laufer, B., & Nation, P. (1999). A vocabulary-size test of controlled productive ability.?Language testing,?16 (1), 33-51.Leech, G., Rayson, P., & Wilson, A. (2001). Word Frequencies in Written and Spoken English: Based on the British National Corpus. Longman: London.Lehecka, T. (2015). Collocation and colligation. In Handbook of Pragmatics Online. Amsterdam: John Benjamins Publishing.Lei, L., & Liu, D. (2016). A new medical academic word list: A corpus-based study with enhanced methodology. Journal of English for Academic Purposes, 22, 42-53.Lessard-Clouston, M. (2013). Word lists for vocabulary learning and teaching. CATESOL Journal, 24 (1), 287-305.Lessard-Clouston, M. (2016, November 18). New Word Lists for Vocabulary Teaching. Paper presented at 47th Annual CATESOL Conference, San Diego: U.S.A.Lewis, M. (1997).?Implementing the lexical approach.?Hove, England: Language Teaching Publications.Lewis, M. (2000). Materials and resources for teaching collocation.?In Teaching collocation: Further development in the lexical approach (pp.186-204). Hove: Language Teaching Publications.Li, E. S. L., & Pemberton, R. (1994). An investigation of students' knowledge of academic and subtechnical vocabulary. In Proceedings joint seminar on corpus linguistics and lexicology, Guangzhou and Hong Kong (pp. 183-196). Hong Kong: HKUST Language Center.Li, Y., & Qian, D. D. (2010).Profiling the Academic Word List (AWL) in a financial corpus. System,?38 (3), 402-411.Lynn, R.W. (1973). Preparing word lists: a suggested method. RELC Journal, 4 (1), 25-32. Martínez, I. A., Beck, S. C., & Panza, C. B. (2009). Academic vocabulary in agriculture research articles: A corpus-based study.?English for Specific Purposes,?28 (3), 183-198. McKenny, J. A. (2006).?A corpus-based investigation of the phraseology in various genres of written English with applications to the teaching of English for academic purposes (Doctoral dissertation). Retrieved from , G., & Ratcliff, R. (2016). Adults with poor reading skills: How lexical knowledge interacts with scores on standardized reading comprehension tests.?Cognition,?146, 453-469.Meara, P., & Nation, I.S. P. (2013). Vocabulary. In?An introduction to applied linguistics?(pp. 44-62). Routledge.Miller, D. & Biber, D. (2015). Evaluating reliability in quantitative vocabulary studies. International Journal of Corpus Linguistics, 20 (1), 30-53.Miller, L. M. & Roodenrys, S. J. (2012). Serial recall, word frequency, and mixed lists: The influence of item arrangement. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38 (6), 1731-1740.Mudraya, O. (2006). Engineering English: A lexical frequency instructional model. English for Specific Purposes, 25 (2), 235-256.Nagy, W. E. (1988). Vocabulary instruction and reading comprehension.?Centre for the Study of Reading Technical Report; no. 431.Nation, I.S.P. (1990). Teaching and Learning Vocabulary. New York: Newbury House.Nation, I.S.P. (2000). Learning vocabulary in lexical sets: Dangers and guidelines. TESOL Journal,?9 (2), 6-10.Nation, I. S. P. (2001). Learning vocabulary in another language.?Cambridge: Cambridge University Press.Nation, I.S.P. (2004). Vocabulary in a second language: Selection, acquisition, and testing. In P. Bogaards &?B. Laufer (Eds.). A study of the most frequent word families in the National British Corpus (pp. 3-13). Amsterdam/Philadelphia: John Benjamins Publishing.Nation, I.S.P. (2012). The BNC/COCA word family lists 25,000. Retrieved from , I. S. P. (2016). Making and Using Word Lists for Language Learning and Testing. Amsterdam: John Benjamins.Nation, I.S.P., & Heatley, A. (2005). RANGE [computer software]. Retrieved from: , I.S.P., & Waring, R. (1997). Vocabulary size, text coverage and word lists. Vocabulary: Description, acquisition and pedagogy,?14, 6-19.Nation, I. S., & Webb, S. A. (2011).?Researching and analyzing vocabulary. Heinle; Cengage Learning. Partington, A. (1998).?Patterns and meanings: Using corpora for English language research and teaching?(Vol. 2). Amsterdam: John Benjamins Publishing.Praninskas, J. (1972). American university word list. London: Longman.Qi, H. (2016). A Corpus-based Comparison between the Academic Word List and the Academic Vocabulary List". Electronic Thesis and Dissertation Repository. 3721.Raupach, M. (1984). Formulae in second language speech production.?Second language productions, 114-137.Schmitt, N. (2004).?Formulaic sequences: Acquisition, processing, and use. Amsterdam: John Benjamin Press.Shabani, G. (2016). A comparative study on the effect of contrived vs. authentic texts on Iranian intermediate EFL learners’ lexical collocations learning. Journal of Studies in Education, 6 (1), 20-36.Smith, C. (2005). The Lexical Approach: Collocation in High School English Language Learners. Oregon: George Fox University. Thorndike, E. L., & Lorge, I. (1944). The teacher's handbook of 30,000 words. New York: Columbia University Teachers' College Press.Timmis, I. (2015). Corpus linguistics for ELT: Research and practice. London/New York: Routledge.Tono, Y. (2002).?The role of learner corpora in SLA research and foreign language teaching: The multiple comparison approach?(Doctoral dissertation, University of Lancaster).Townsend, D., Filippini, A., Collins, P., & Biancarosa, G. (2012). Evidence for the importance of academic word knowledge for the academic achievement of diverse middle school students.?The Elementary School Journal,?112(3), 497-518.Vongpumivitch, V., Huang, J. Y., & Chang, Y. C. (2009). Frequency analysis of the words in the Academic Word List (AWL) and non-AWL content words in applied linguistics research papers.?English for Specific Purposes,?28 (1), 33-41.Wang, J., Liang, S., & Ge, G. (2008). Establishment of a Medical Academic Word List. English for Specific Purposes, 27, 442-458.Ward, A. E. (2009).?A Formative Study Investigating Interactive Reading and Activities to Develop Kindergartners' Science Vocabulary. California: ProQuest LLC.Webb, S., Sasao, Y., & Balance, O. (2017). The updated Vocabulary Levels Test: Developing and validating two new forms of the VLT. International Journal of Applied Linguistics, 168:1, 33–69.West, M. (1953). A general service list of English words. London: Longman.Wickens, C. D. (1989). Attention and skilled performance. In D. H. Holding (Ed.), Human Skills (2nd Ed.) (pp. 71-105). New York: John Wiley.Woolard, G. (2000). Collocation: Encouraging learner independence. In M. Lewis (Ed.), Teaching collocation: Further developments in the lexical approach (pp. 28-46). Hove: Language Teaching Publications.Xue, G., & Nation, I. S. P. (1984). A university word list.?Language learning and communication, 3 (2), 215-229.Ying, Y., & Jingyi, J. (2014). Incorporating Collocation Teaching in a Reading-Writing Program. ELT World Online, 2014, 1-17.FundingThe authors received no financial support for the research, authorship, and/or publication of this article.Appendix A: The medical academic word list of textbooks (MAWLOT) with their frequency and rangeNumberWordFrequencyCoverage (%)Range1abdomen 6020.017312abnormal 7350.021253abscess 3910.011314abuse 16910.048315acid 14510.041316acidemia 1790.005257acidosis 2020.006318acute 20140.057319adenocarcinoma 1200.0032510adolescent 18720.0533111adrenal 5870.0163112adrenergic 1690.0052513aggressive 3940.0113114airway 4390.0123115albumin 1500.0043116alcohol 7790.0223117allergy 2660.0072518alveolar 1100.0033119amino 2690.0073120amnesia 1380.0042421amniocentesis 1470.0042522amniotic 3900.0112523anal 3450.0093124analgesia 1460.0043125anastomosis 1920.0051926anatomic 1940.0051927anatomy 2170.0063128androgen 1710.0053129anemia 3850.0113130anesthesia 1750.0053131anomaly 2810.0083132anorexia 3560.0092533antagonist 1350.0043134antenatal 1020.0032535anterior 4260.0123136antibiotic 3710.0103137antibody 6000.0173138antigen 3520.0103139aorta 1610.0053140apnea 2000.0063141appendicitis 2140.0062542artery 8810.0253143arthritis 4150.0123144aspire 1610.0052545asthma 4000.0113146asymptomatic 3190.0093147ataxia 1640.0042549 atresia 1420.0041950atrophy 1420.0043151atypical 1410.0043152aureus2120.0051953autistic 4340.0122554autoimmune 2890.0083155 autonomic 139?0.0043156autosome 2210.0062457axis 2200.0063158bacterium 2260.0062559barium 1430.0042560basal 2990.0083161baseline 1480.0043162benign 2620.0073163bicarbonate 2330.0063164bilateral 2360.0063165bile 2970.0082566biliary 2220.0062567bilirubin 1570.0052568biochemical 1500.0043169biology 1630.0043170biologic 1690.0051971biopsy 4020.0113172bladder 2460.0073173bowel 10760.0303174bulimic1280.0031875cancer 18730.0533176capillary 1700.0053177carbohydrate 1120.0033178carcinoid 1000.0031979carcinoma 4320.0123180cardiac 7670.0213181cardiovascular 3630.0063182catheter 2500.0073183cavity 3270.0093184cell 34280.0983185cerebral 5750.0163186cervix 3830.0063187cesarean 5290.0152588chemotherapy 4220.012?3189cholesterol 2870.0083190chromosome 5350.0153191chronic 18630.0533192circulate 2580.0072593cirrhosis 2320.0062594cleft 1350.0043195clinic 21070.0603196clot 1160.0032497coagulate 2430.0062598coli 1190.0032499colitis 292?0.00625100colon 6630.01831101colorectal 1960.00519102coma 1580.00425103congenital 11200.03231104conjunctivitis 1220.00331105constipate 1350.00425106contraceptive 2510.00731107contraction 3560.00631108cord 6220.01831109coronary 2270.00631110cortex 1100.00331111corticosteroids 1870.00531112cortisol 2940.00831113crisis 1380.00431114crohn 3580.0125115cushing 1280.00319116cutaneous 2230.00631117cyst 2000.00525118cytokines 1560.00531119defect 9320.02631120deform 1320.00425121dehydrogenase 1070.00325122dementia 1480.00425123dense1190.00331124depress 4570.01325125dermatitis 2730.00825126diabetes 10410.03031127diagnose 3765?0.1031128diameter 2000.00531129diarrhea 5290.01531130diet 4600.01331131differential 6720.01931132diffuse 1530.00431133dilate 1060.00325134disabled 1690.00425135discharge 1910.00531136disorder 84570.24131137dissect1310.00325138distal 3990.01131139distention 1140.00325140distress 6550.01831141donor 1560.00431142dopamine 1380.00331143doppler 1030.00325144dose 18190.05131145drainage 2640.00731146drug 19790.05631147duct 1520.00425148duodenum 1290.00325149dysfunction 9930.02831150dysplasia 1870.00525151ectopic 1780.00531152edema 4480.01231152effusion 1270.00331124ejaculate1270.00319155elective 2000.00631156electrolyte 2030.00631157emergency 4990.01431158encephalopathy 1810.00531159endocarditis 1260.00325160endocrine 2650.00731161endogenous 1610.00431162endothelial 2420.00725163enuresis 2510.00719164enzyme 5410.01531165epidemiology 4770.01231166epidural 2160.00631167epilepsy 1480.00425168epinephrine 1350.00331169epithelium 1550.00431170erectile 1060.00325171erythema 2010.00631172erythematosus 1870.00531173erythrocyte 1890.00531174erythropoietin 1010.00219175esophageal 5120.01431176esophagus 4940.01431177estrogen 1340.00331178etiology 4780.01431179 excise1540.00431180excrete108?0.00231181exogenous 1340.00331182extracellular 1160.00331183facial 1030.00231184fascia 1380.00331185fatal 1860.00531186fatigue 3930.01131187febrile 1080.00331188fecal 1050.00331189feedback 1430.00431190femoral 1310.00324191fertile 1590.00424192fetal 34980.1031193fetus 7420.02131194fibrosis 2520.00731195fistula 2360.00731196focal 1590.00431197folate 2780.00731198folic 1100.00331199follicle 1610.00425200forceps 1330.00431201foster 3010.00831202fracture 2780.00731203fungus1020.00325204gastric 9100.02631205gastroesophageal 1230.00325206gastrointestinal 5910.01731207gene 11930.03431208genital 3050.00831209gestation 4950.01431210gland 2570.00731211glomerular 1030.00331212glucocorticoid 1190.00331213glucose 9660.02731214glycogen 1040.00318215gonadotropin 1240.00331216graft 1050.00331217healthcare 1600.00431218hematopoietic 1050.00324219hemoglobin 1380.00325220hemorrhage 4670.01325221hemolytic 3650.0131222hepatic 4140.01131223hereditary 2450.00731224hernia 4470.01331225herpes 1250.00331226hip 1260.00325227homosexual 1380.00424228hormone 12090.03431229hydrops 1030.00319230hyperactive 2360.00725231hyperglycemia 1310.00331232hypoglycemia 3060.00931233hyperplasia 1630.00431234hypertension 7390.02131235hypertrophy 1460.00431236 hypotensive 1540.00431237hypothalamic 2230.00625238hypothalamus 1290.00325239hypothyroidism 2820.00825240hypoxic 2030.00625241hysterectomy 1490.00425242idiopathic 1190.00331243immune 5630.01631244immunodeficiency 1840.00631245immunoglobulin 2030.00625246impair 7590.02131247impulse 1990.00625248inborn 1090.00325249incise 1120.00319250infant 20000.05731251infract1080.00331252infect 11480.03225253inferior 1200.00331254inflame 2890.00825255infuse 1710.00525256ingestion 1920.00625257inguinal 2510.00725258inherit 1890.00625259inject 1040.00325260inpatient 1040.00325261insipidus 1620.00525262insomnia 2240.00618263insulin 11150.03131264intake 5590.01631265intellectual 2060.00625266intercourse 1710.00525267intermittent 1660.00531268interstitial 1280.00331269intestine 1990.00631270intracellular 2050.00631271intracranial 2130.00631272intrauterine 3070.00831273intravenous 3220.00931274invasive 2110.00631275involuntary 1060.00331276irritable 1970.00631277ischemia 1010.00331278jaundice 1380.00431279kidney 2690.00731280kinase 1630.00525281lactate 1410.00431282lateral 2930.00831283lesion 5400.01531284lethal 1290.00331285leukemia2650.00725286ligament 1330.00425287lipid 1280.00431288lipoprotein 1250.00431289liver 9460.02731290lobe 1450.00431291local 1100.00325292loop 1190.00431293lupus 2180.00631294lymph 389?0.01125295lymphadenopathy 243?0.00618296malform 1770.00525297malign 3690.01025298malnutrition 2330.00631299mania 1150.00419300marrow 5960.01731301maternal 15430.04431302measles 1330.00419303meconium 3850.01131304median 1240.00431305medication 9910.02831306melanoma 1360.00431307mellitus 1790.00531308membrane3010.00831309meningitis 2050.00625310menstrual 1880.00531311mesenteric 1150.00425312metabolism 4350.01231313metastasis 1360.00431314molecule 1510.00431315mood 6030.01725316morbid 2330.00625317mortal 2500.00625318motility 1230.00431319mucus 1630.00431320muscle 10200.03031321mutate 3550.01025322myocardial 1930.00631323nasal 3000.00825324nausea 2540.00631325necrosis 2460.00631326neonatal 8710.02425327nerve 4090.01131328nervosa 3070.00818329neural 2050.00631330neurological 4410.01225331neuron 1000.00325332neuropathy 1350.00331333neutropenia 1550.00419334neutrophils 1180.00319335newborn 5260.01525336nocturnal 1460.00425337node 2510.00724338nonspecific 1360.00431339nucleus 1050.00331340nutrition 3840.01031341obese 2850.00831342oblique 1320.00425343obsess 1750.00519344obstetrics 5520.01519345obstruct 3590.01025346occult 1030.00331347optimal 2530.00731348oral 9930.03031349organism 1480.00431350osmolality 1080.00324351outpatient 1780.00531352ovary 3240.00925353overweight 1190.00319354ovulate 1010.00325355oxidase 1100.00331356oxytocin 3230.00925357palate 1190.00331358palliative 2640.00725359palsy 1860.00531360pancreatic 1780.00531361panic 3510.01019362parenteral 1980.00631363parietal 1290.00324364patch 1320.00431365pathogenesis 1410.00431366pathology 2230.00631367pathophysiology 1440.00431368patient 89430.2531369pediatrics 5650.01525370pelvis 1120.00331371penicillin 1800.00519372peptide 1990.00631373perforate1240.00619374prenatal4110.01131375periphery 3350.00925376peritoneal 2330.00625377pervasive 2190.00619378phenotype 1790.00531379physiology 1100.00331380pica 1090.00318381pituitary 6810.02031382placebo 1870.00531383placenta 6100.01725384plasma 8820.02531385platelet 8380.02331386pneumonia 3160.00931387polar 2280.00619388posterior 2050.00325389precursor 1420.00431390pregnant 14450.04131391premature 2200.00624392prenatal 3350.00931393prescribe 1450.00425394preterm 1840.00525395prevalent 3580.01031396progesterone 1210.00325397prognosis 6240.01725398prolactin 1200.00325399prophylaxis 2230.00625400protein 13960.04031401proteinuria 1170.00319402proximal 1470.00431403psychiatric 14620.04226404psychosis 1620.00524405psychosocial 1140.00319406psychotherapy 4660.01319407puberty 3400.01025408pulmonary 2660.00725409pulse 2050.00331410purpura 1600.00419411radiate 1500.00425412radiograph 2060.00324413rash 2720.00725414receptor 8850.02531415recessive 3390.00925416recur 6350.01825417reflex 2060.00631418regimen 2310.00624419relapse 1310.00425420remission 1030.00319421renal 10160.03031422reproductive 1310.00431423respirator 6300.02025424resuscitate 1740.00526425retard 6560.01926426rheumatoid 1360.00419427rhinitis 1950.00519428rubella 1380.00425429rupture 2810.00719430scan 1500.00425431scar 1010.00325432schizophrenic 1600.00419433sedate 1710.00525434segment 1250.00425435seizure 6050.01725436sensation 1130.00331437sensory 1820.00525438sepsis 2100.00625439serotonin 1410.00425440serum 11560.03325441shunt 1290.00419442sickle 2380.00719443sinus 1590.00431444skeletal 1460.00431445smear 1020.00325446soluble 1080.00331447somatic 2090.00625447spine 3520.01025449spontaneous 4360.01231450staphylococcus 1340.00419451stenosis 1630.00525452steroid 2860.00725453stimulate 5210.01431454stool 2090.00325455streptococcus 2550.00725456stutter 1260.00419457subcutaneous 1580.00531458superior 1160.00331459supplement 1050.00325460suppress 2350.00631461surgeon 1170.00325462surgery 7420.02131463surveillance 1070.00325464susceptible 2440.00731465symptom 26510.07525466syndrome 35190.10031467synthesis 4240.01231468syphilis 1270.00425469systemic 6470.01831470tachycardia 1990.00531471testosterone 2680.00725472thalassemia 1600.00329473therapy 35260.10031474thrombosis 2840.00819475thyroid 7930.02231476tibia 1210.00519477tissue 10370.03031478tone 1660.00431479toxin 1050.00319480tract 4630.01219481transfuse 5470.01519482transient 2770.00831483transplant3730.01019484trauma , 8540.02431485treat 1460.00425486trimester2780.00731487tuberculosis 2590.00731488tubular1000.00331489tumor 3410.01025490ulcer 1200.00419491ultrasound2960.00831492umbilical 1950.00519493unilateral1180.00325494utero 1670.00431495vaccine 2930.00825496vagina 4270.01225497vascular 5510.01531498vasopressin2030.00625499vein 1660.00425500venous 2580.00725501ventricle 1080.00319502virus 6050.01731503vomiting 1450.00425504vulnerable 1600.00431505warrant 1570.00431Appendix B: The Medical Academic Collocation List of Textbooks (MACL)No.Word Type of wordCollocation line example1PATIENT Nin N with nevidence is seen, it is diagnostic. In patients with a compatible clinical syndrome, exclN with ntesting, both antibodies were found. In general, a patient with LAC has higher levels of2DISORDERNadj N than would be predicted for the simple disorder, an additional process has to be presentN andif a woman's father has the disorder and her mother is a carrier, sheN is characterized byautistic disorder autistic disorder autistic disorder is characterized by marked impairment inN is associated withattening of schizophrenia itself. This depressive disorder is associated with an increased risk ofN is pppresent with the same intensity, a neuromuscular disorder is suspected. When a patient is found N is nDisorder The necessary feature of bipolar I disorder is a history of a manic orN is adjciated with psychosis. The etiology of autistic disorder is unknown. There is an increased risk3DIAGNOSISNN of acute MImes Clinical Manifestations Traditionally, the diagnosis of acute MI has rested on theN of acute nof cough is useful for considering the diagnosis of acute bronchitis. Patients usually seN of nirregularity of the RR wave makes the diagnosis of AF relatively easy. Aberrant conductiN of adjg continues and hypovolemia becomes significant. Diagnosis of Ectopic Pregnancy Laboratory TestsN of acuteuired. Transbronchial biopsy may be performed for diagnosis of acute rejection. Acute rejection usualN of acute abdominal painSevier differential diagnosis the differential diagnosis of acute abdominal pain is extensive. CoN of acute appendicitis90% and a specificity of 80% to 90% for the diagnosis of acute appendicitis among patients withN of adrenal insufficiencyAs is true for most endocrine disorders, diagnosis of adrenal insufficiency depends on mainN is based onan elevated C-reactive protein level. The diagnosis is based on the patients’ history andN is confirmed byg, tenesmus, and occasionally abdominal pain. The diagnosis is confirmed by a careful history andN is establishedright ventricle and pulmonary artery. After the diagnosis is established, patients should undergoN is madeisease represents a surgical emergency. After the diagnosis is made, operation is performed in anN is adjIf no cause can be determined, the diagnosis is idiopathic precocious puberty; this cN is made bythe differential diagnosis for hyperkalemia. The diagnosis is made by demonstrating elevated serum4THERAPYNN for acutembocytopenic purpura Immune globulin, intravenous Therapy for acute episodes Botulism Trivalent A, BN for adjfor chelation therapy. The role of chelation therapy for asymptomatic individuals with mercuryN for nutility of this approach in developing targeted therapy for breast and ovarian cancer. AccuracyN is ppndotracheal and tracheostomy tubes. If aggressive therapy is considered appropriate, ligation of theN is adjIA trachomatis. Thus, azithromycin or doxycycline therapy is appropriate empirical therapy. AndrewN is ns. Couples (Marital) Therapy Couples or marital therapy is a form of psychotherapy designed to5SYNDROMENN is nhydroxysteroid dehydrogenase. The cause of the syndrome is activation of the mineralocorticoid reN is pptation, and respiratory distress. Generally, the syndrome is considered to be mild and self-N is pp pre follow surgical procedures or immunizations. The syndrome is thought to be immune-mediated fromN is associated withleukocyte adhesion deficiency, whereas hyper-IgE syndrome is associated with cold abscesses, eczemaN is caused bysyndrome, also termed ACTH-independent Cushing's syndrome is caused by autonomous adrenal cortisolN is characterized byately 1 in 10,000 live-born infants, Prader-Willi syndrome is characterized by hypotonia of prenatalN is adjnomegaly and anemia are present. Crigler-Najjar syndrome is a serious, rare, permanent deficiency6FETALADJprep cells of ADJ originclinical benefit after therapy with cells of fetal origin that has now lasted up to 10ADJ heart nMaternal corticosteroid administration to treat fetal heart block is controversial. Shinohara andADJ heart ratethe neonatal CNS. If recovery of the fetal heart rate occurs as a result ofADJ heart rate abnormalitiessly, as should uterine contractions during labor. Fetal heart rate abnormalities may indicate baselyADJ heart rate accelerationinfarction in 93 percent. Thus, the lack of fetal heart rate acceleration, when not due toADJ alcohol syndromeforms of brain developmental disorder, including fetal alcohol syndrome and Down syndrome, and induring ADJ developmentmusculature to unite in the midline during fetal development. The umbilical vessels may be spduring ADJ lifeprecursors to migrate into the distal bowel during fetal life. The aganglionic distal segment7CELLNT N activationthe secondary wave that ensues after T-cell activation, can also induce expression of HLAB N activationhematopoiesis IL-4 T cells, mast cells B-cell activation, IgE switch, inhibition of TH1 cellsickle N diseaseiabetes mellitus Malnutrition Uremia Sickle cell disease Zinc deficiency Multiple carboxylsickle N anemia of Southeast Asian or African ancestry; and sickle-cell anemia for people of African, Mediterrsickle N nhemoglobin variants, such as hemoglobin C or sickle cell –thalassemia. Sickle cell disease resislet N tumorswill await multi-institutional trials. All islet cell tumors except insulinomas (10%) and GRFomasgerm N tumorsrelated to recurrent pneumonitis. Although germ cell tumors are rare, the diagnosis can besquamous N carcinomas of the ncultures have a high rate of squamous cell carcinoma of the esophagus, not related tosquamous N carcinomasinclude sunburn, skin cancers (basal cell and squamous cell carcinomas and, to a lesser extent,n N ncystic carcinoma (colloid carcinoma), signet ring cell carcinoma, adenosquamous carcinoma, naplasticadj N carcinomanosquamous carcinoma, anaplastic carcinoma, giant cell carcinoma, and sarcomatoid carcinoma are cons8CLINICNadj Nto the success of the program is that maternal clinic coordinates all aspectsN and adjent perturbations in neuronal function. Thus, the clinical and neurologic consequences of hypertonicN manifestationsross the duodenum, causing partial obstruction. Clinical manifestations about 60% of children wiN manifestations prepsyndrome toxin-1\x97TSST-1\x97causes the clinical manifestations by provoking profound endoN manifestations of (1995), are that there is no correlation between clinical manifestations of disease and complement 9ACUTEADJADJ adjlready administered. Intoxication may result from acute, chronic, or acute-on-chronic exposure. A an ADJ abdomenAbdominal pain occurs frequently and can mimic an acute abdomen. The presence of polyuria, despiteprep ADJ abdomenin a large series of patients with acute abdomen. A good review of this importantprep ADJ abdominal painematoma is an uncommon condition characterized by acute abdominal pain and the appearance of anADJ stress disorder, PTSD, and acute stress disorder. PTSD and acute stress disorder have the nature of theADJ stress disorder, PTSDgroup of trauma spectrum disorders that includes acute stress disorder, PTSD, and somatization disoADJ respiratory acidosisderlying cause and ensuring adequate ventilation. Acute respiratory acidosis can be very dangerous,ADJ respiratory alkalosis patient with hypophosphatemia as a result of acute respiratory alkalosis do not require treatmADJ respiratory distress syndromeDiffuse alveolar filling process similar to the acute respiratory distress syndrome diffuse alveolADJ renal failurekidney dysfunction, but they only rarely cause acute renal failure. Acute renal failure may be10INFANTNN mortalityrenal tract malformations are important causes of infant mortality and of morbidity in older childrenN mortalityrateracial/ethnic minorities has persisted, and the infant mortality rate in African-American women isadj Nof cyanosis is required for every cyanotic infant after prompt administration of oxygen, withN is ppto prevent perinatal HIV transmission; if an infant is confirmed as HIV-infected while receivinthe N is -ingmouth. Although the growth rate of the infant is decreasing, energy needs for activity inthe N is adjthan the mother's titer, if the infant is symptomatic, or if the mother has11DRUGNN interactionsIV N abuse to serum proteins and has no significant drug interactions. As with donepezil, galantamine -24250489888800occurs with endocarditis, osteomyelitis, and IV drug abuse. Splenic abscess may also occur asN is pp prepgiven at least twice daily. If the drug is stopped for more than 48 hours, the12CANCERNprep n N in pl-nall ages, whereas the estimates for breast cancer in women and for thyroid cancer inN is adjvaluating a child with suspected cancer. Childhood cancer is rare; only about 1% of new cancer N is adj n an imprint of elsevier lung cancer lung cancer is a significant public health problem inN is pptherapies have gained popularity in recent years; cancer is attributed to a defective immune system,N is pp prep relative risk of cervical dysplasia and cervical cancer is increased in current COC users, butN in nrecent reports from Europe suggest excess brain cancer in cell phone users. These data areN in patients withwith an 8-fold increased incidence of colorectal cancer in patients with polyps who do not13ADOLESCENTADJADJ nTd if they have not received an adolescent Td booster. For patients who have receiADJ pl-nvictims of physical violence by their boyfriends; adolescent boys may become victimizers. Although sADJ pregnancybe ruled out in all cases of adolescent pregnancy. When pregnancy is confirmeADJ boyson the street are informal and spontaneous. Adolescent boys frequently have contact with the lADJ girls v with masturbation, and more than half of adolescent girls report masturbation. The balanceor ADJ antisocial behaviour ctual functioning, academic problem, childhood or adolescent antisocial behaviour, and identity probl14CHRONICADJacute or ADJin suggested texts or literature searches. In acute or chronic cases, information about what theprep acute or ADJpanel (electrolytes, liver enzymes, BUN) Signs of acute or chronic hepatic, renal, adrenal dysfunctiprep ADJ obstructive lung diseasetis, synovium in rheumatoid arthritis, alveoli in chronic obstructive lung disease, and colonic epitADJ obstructive pulmonary diseasenditions (aortic atherosclerosis, renal function, chronic obstructive pulmonary disease, and coagulaprep ADJ obstructive pulmonary diseasecan be misleading. Suppose a patient with chronic obstructive pulmonary disease and a historprep ADJ kidney disease therapeutic options are much more limited for chronic kidney disease, it is less likely toprep ADJ renal failurehyperplasia. As many as 90% of patients with chronic renal failure have evidence of secondary Hprep ADJ fatigue syndromeon (CDC) defined specific diagnostic criteria for chronic fatigue syndrome. Since then, the disorderprep ADJ pancreatitiscan develop. Many of the patients with chronic pancreatitis are alcoholics who, even before15DOSENadj N of narrangements. You will be given a small dose of medication that will make you sleepy16ABUSENN treatmentpsychiatric problems. The major goals of drug abuse treatment are detoxification, abstinence iniprep n Nected individuals often have members with alcohol abuse and anxiety disorders. Provocative conditionSubstance N treatmentance Use Among adolescents enrolled in substance abuse treatment programs, 96 percent are polydrug a drug of Ndirect physiologic effects of a drug of abuse, a medication, or a general medical conditionsexual N of adult child, physical abuse of adult, and sexual abuse of adult) that frequently are the focussexual N of childTR) lists physical abuse of child, sexual abuse of child, and neglect of child. Thesexual N of childrenData are from Finklehor D. The sexual abuse of children: current research reviewed. Psychephysical N of adultof clinical attention is child neglect. Physical abuse of adult this category should be used whenphysical N of childabuse or neglect includes five problems (physical abuse of child, sexual abuse of child, neglect17MATERNALADJprep ADJ nrepresents one of the leading causes of maternal death in the United States. It occursprep ADJ adjch with contractions, (3) bearing-down efforts of maternal abdominal muscles, and (4) extension andprep the ADJ abdomenFor example, therapeutic radiation doses to the maternal abdomen are contraindicated because of aprep ADJ serumleak into the amnionic fluid, resulting in maternal serum AFP levels that may be dramatically18PSYCHIATRICADJprep ADJ conditions prepspectrum of diseases and disorders, ranging from psychiatric conditions to cancer, osteoporosis, anADJ conditions vn, acute nonorganic psychotic disorders, or other psychiatric conditions is indicated.ADJ pl-nThe clinician must rule out medical and psychiatric conditions that could mimic these sympn of ADJ disorders(2003) reported a 14-percent point prevalence of psychiatric disorders during pregnancy. Unfortunatepl-n prep ADJ disorderscontrast to the insomnia in patients with psychiatric disorders, daytime adaptation is generprep ADJ symptoms prepsetting. Because of the high risk for psychiatric symptoms in abused and neglected child19ACIDNN base balanceor alkalemia. It is customary to define acid-base balance in terms of the hydrogenn N concentrations blood as hemoglobin and plasma proteins. Amino acid concentrations are higher in the fetal thanLysergic N diethylamide (LSD)Classically known as lysergic acid diethylamide (LSD), these are amine alkaloids obtained only thron N nllowed up by performing quantitative plasma amino acid analysis, measuring phenylalanine and tyrosinprep n Novarian cancers. Autoantibodies to -aminobutyric acid (GABA)-ergic neurons in the serum andN base disordersalkalosis (Table 37-5). Treatment of respiratory acid-base disorders focuses on correction of theN base disturbancewhen there is more than one primary acid-base disturbance. An infant with bronchopulmoN base disturbancesThe mechanisms for changes in potassium in acid-base disturbances are not completely understo20PROTEINNactivated N Cpatients with a high risk of death, activated protein C therapy also improves survivapercent N boundaffect its absorption, and it is 55 percent protein bound in the plasma; 94 percent of lamotriN and neither transudates or exudates based on fluid protein and lactate dehydrogenase (LDH) concentrateNote: N=noun, V=verb, ADJ=adjective, PL-N=plural noun, PREP=prepositionAppendix C: The particular patterns of the top four medical technical words of the MAWLOTNo.WordType of wordCollocation line example1CESAREANNdelivery by N sectionsecond born of twins, and delivery by cesarean section without labor. RDS may developN sectionsupport during labor reduces the rate of cesarean section and forceps deliveries, the needprep N section small infants and an increased requirement for cesarean section. If tumor extends into the renalnumber of N deliverye or more changes 30 percent increased number of cesarean deliveries 26 percent stopped perfrisk of N deliveryduction with cervical ripening increases the risk of cesarean delivery in multiparous women. Obstetrate of N delivery,Hamar and associates (2001) found that the rate of cesarean delivery following elective inductionvaginal birth after N deliverynecologists: Induction of labor for vaginal birth after cesarean delivery. Committee Opinion No. 271in women with a prior to N delivery50 percent of placentas in women with a prior cesarean delivery had adhered myometrial fib2ESOPHAGEALADJADJ refluxindividuals who are suspected of having recurrent esophageal reflux and aspiration events at night.ADJ atresiawith a number of abnormalities. These include esophageal atresia, diaphragmatic hernia, and abdominalADJ varicesatresia. After this age, peptic disease and esophageal varices continue to be common causes ofadj ADJ pl-nfunctional esophageal disorderother nonspecific esophageal symptoms Functional gastroduodenal dison prep ADJ cancernting with esophageal cancer. The distribution of esophageal cancer across gender, age, and race is ADJ motility disordersdiagnosis and will help eliminate other potential esophageal motility disorders. In typical achalasiN prep ADJ varices varices most commonly occur in association with esophageal varices, but they occasionally occur alN of the ADJ bodyof information about the function of the esophageal body and the LES may be obtained3ETIOLOGYNthe N of nto the prostate gland. Theories for the etiology of type III prostatitis include infectiousthe N of adjto approximately one per day. Etiology The etiology of functional constipation and soiling inadj Ncareful bleeding history may suggest a specific etiology, consideration must always be given to pr4ANEMIANN is adjTransfusion of erythrocytes and erythropoietin anemia is common in septic shock, but the N and nnes leads to hydrops fetalis, severe intrauterine anemia and death, unless intrauterine transfusionN with n be discovered incidentally to a severe hemolytic anemia with growth failure, splenomegaly, and chroNote: N=noun, V=verb, ADJ=adjective, PL-N=plural noun, PREP=prepositionAppendix D: The list of the textbooks to develop the Medical Academic CorpusComprising thirty-one medical sub-disciplines with two textbooks being selected for each, had around 3.5 million running words in total. Due to the limited space of words, the 62 medical textbooks included in the Medical Academic Corpus are, for example: Harrison’s Principles of Internal Medicine (2018); Cecile Essentials of Medicine (2106); Sabastian textbook of surgery (2016); CURRENT Diagnosis & Treatment: Surgery (2015); Nelson essentials of pediatrics (2015); Current Diagnosis & Treatment: Pediatrics (2018); Williams Obstetrics (2014); CURRENT Diagnosis & Treatment: Obstetrics & Gynecology (2019); Comprehensive Gynecology (2013); Cardiology: An Integrated Approach (2018); CURRENT Diagnosis & Treatment: Cardiology (2017); Current Diagnosis & Treatment: Psychiatry (2019); Kaplan & Sadock’s synopsis of psychiatry: behavioural Sciences/Clinical Psychiatry (2014); Fitzpatrick's Dermatology (2019); Clinical Dermatology (2013); Clinical Neurology (2018); Case Files: Neurology (2018); Practical Office Orthopedics (2018); Current Diagnosis & Treatment in Orthopedics (2014); The Colour Atlas and Synopsis of Family Medicine (2019); CURRENT Diagnosis & Treatment: Family Medicine (2015); CURRENT Diagnosis & Treatment: Gastroenterology, Hepatology, & Endoscopy (2016); CURRENT Diagnosis & Treatment: Nephrology & Hypertension (2018); Smith & Tanagho's General Urology (2013); Oxford handbook of Urology (2013); CURRENT Diagnosis & Treatment: Emergency Medicine (2017); Case Files: Emergency Medicine (2017); Treatment: Rheumatology (2013); Current Medical Diagnosis & Treatment (2019); Principles of Rehabilitation Medicine (2018); Proprioception in Orthopaedics, Sports Medicine and Rehabilitation (2018); Williams Hematology (2016); Hoffbrand's Essential Haematology (2015); Simpson's Forensic Medicine (2019); Textbook of Forensic Medicine & Toxicology: Principles & Practice (2014); Respiratory: An Integrated Approach to Disease (2015); Fishman's Pulmonary Diseases and Disorders (2015); Textbook of Veterinary Diagnostic Radiology (2018), Introduction to Diagnostic Radiology (2015); Asthetic and Restorative Dentistry: Material Selection and Technique (2013); Clinical Problem Solving in Dentistry: Orthodontics and Paediatric Dentistry (2016); Morgan & Mikhail's Clinical Anesthesiology (2018); Annual Update in Intensive Care and Emergency Medicine (2018); Review of Medical Microbiology & Immunology: A Guide to Clinical Infectious Diseases (2018); CURRENT Diagnosis & Treatment: Nephrology & Hypertension (2018); Medical Epidemiology: Population Health and Effective Health Care (2015); Laposata's Laboratory Medicine: Diagnosis of Disease in the Clinical Laboratory (2018); Anderson Manual of Medical Oncology (2016); Vaughan & Asbury's General Ophthalmology (2018). ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download