医学英语高频词汇

Jing Wang,Shao-lan Liang,Guang-chun Ge *

Department of Foreign Languages,Fourth Military Medical University,Xi’an,China

Abstract

This paper reports a corpus-based lexical study of the most frequently used medical academic vocabulary in medical research articles (RAs).A Medical Academic Word List (MAWL),a word list of the most frequently used medical academic words in medical RAs,was compiled from a cor-pus containing 1093011running words of medical RAs from online resources.The established MAWL contains 623word families,which accounts for 12.24%of the tokens in the medical RAs under study.The high word frequency and the wide text coverage of medical academic vocabulary throughout medical RAs confirm that medical academic vocabulary plays an important role in med-ical RAs.The MAWL established in this study may serve as a guide for instructors in curriculum preparation,especially in designing course-books of medical academic vocabulary,and for medical English learners in setting their vocabulary learning goals of reasonable size during a particular phase of English language learning.

Ó2008The American University.Published by Elsevier Ltd.All rights reserved.

1.Introduction

The acquisition of vocabulary has long been considered to be a crucial component of learning a language (Coady,Magoto,Hubbard,Graney,&Mokhtari,1993;Nation,2001)because the breadth and depth of a student’s vocabulary will have a direct influence upon the descriptiveness,accuracy and quality of his or her writing (Read,1998).Nagy (1988)also claimed that vocabulary is a major prerequisite and causative factor in compre-hension.The dramatically large number of English words,however,is a learning goal far 08-4906/$34.00Ó2008The American University.Published by Elsevier Ltd.All rights reserved.doi:10.1016/j.esp.2008.05.003

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The article is co-authored equally.*Corresponding author.Tel.:+862984774475;fax:+862983234516.

E-mail address:guangcge@fmmu.edu.cn (G.-c.Ge).

Available online at www.sciencedirect.com

English for Specific Purposes 27(2008)

442–458www.elsevier.com/locate/esp

NGLISH FOR S PECIFIC P

Fortunately,all words are not equally important in different stages of learning.Nation’s (2001)division of vocabulary into four levels—high frequency words,academic vocabu-lary,technical vocabulary and low frequency words—indicates that some words deserve more attention and effort than others in different phases of language learning or for differ-ent purposes.According to Nation and Waring(1997),it is generally agreed that the beginners of English learning should focus on thefirst2000most frequently occurring word families of English in the General Service List(GSL)(West,1953),while for inter-mediate or advanced learners who usually study English for academic purposes,the com-mand of these GSL words may no longer be their major concern and the priority of their vocabulary acquisition may be shifted to lower frequency vocabulary.In academic set-tings,ESP students do not see these technical terms as a problem because these terms are usually the focus of the discussion in the classroom or are glossed in the textbook (Strevens,1973).The vocabulary that ESP students have most difficulty with is known, in ESP jargon,as non-subject-specific semi-technical vocabulary or academic vocabulary (Li&Pemberton,1994;Shaw,1991;Thurstun&Candlin,1998).

1.1.Academic vocabulary

Academic vocabulary,which is also called sub-technical vocabulary(Cowan,1974)or semi-technical vocabulary(Farrell,1990),is viewed as‘‘formal,context-independent words with a high frequency and/or wide range of occurrence across scientific disciplines, not usually found in basic general English courses;words with high frequency across sci-entific disciplines”(Farrell,1990,p.11).The high frequency occurrence of academic words in academic text has been confirmed by some researchers.Sutarsyah,Nation,and Ken-nedy(1994)reported that academic vocabulary accounted for8.4%of the tokens in the Learned and Scientific sections of the LOB and Wellington corpora,and for8.7%of the tokens in economics texts.Coxhead(2000)reported that the academic vocabulary in her Academic Word List covered10%of the tokens in her3500000running word aca-demic corpus.Santos’research(2000)revealed that roughly16%of the words in his text-book samples across different disciplines were academic words.This high coverage of academic words in the academic texts has far exceeded the5%ratio of the unknown to the known comprehension threshold suggested by Laufer(1988),who has pointed out that a learner has to know95%of the words in a text to ensure reasonable comprehension of the text because the ratio of unknown to known words over5%is not sufficient to allow reasonably successful guessing of the meaning of the unknown words.In addition,Kuehn (1996)observed that knowledge of academic words differentiated academically well-pre-pared from under-prepared college students from all backgrounds.Thefindings from these studies clearly indicate that EAP learners,without sufficient knowledge of academic vocabulary,cannot deal effectively with reading materials for various types of academic tasks they are supposed to fulfill(Laufer&Nation,1999).However,proficient use of aca-demic vocabulary is one of the most challenging tasks in ESP students’word expansion. Anderson and Freebody(1981)found that academic words were the words most often identified as unknown by her students in academic texts.Based on his study,Farrell (1990)reported that the lack of knowledge was partly the result of the assumption of some444J.Wang et al./English for Specific Purposes27(2008)442–458

subject teachers that their students knew these words and as a result they seldom taught these words explicitly.

1.2.Previous studies on academic vocabulary list development

Previous studies on academic vocabulary have produced some very helpful academic word lists.Quite a number of these academic word lists focused on the academic vocabulary occurring across different disciplines.By analyzing301800words in textbooks and lectures published in journals covering19academic disciplines,Campion and Elley(1971)devel-oped a word list containing500most common words and3200frequently used words. The items in their list represented the vocabulary that students were likely to encounter in their university studies.Praninskas(1972)compiled the American University Word list, which was based on a corpus of272466words from10university-level textbooks covering 10academic disciplines.Lynn’s(1973)and Ghadessy’s(1979)word lists were drawn up by counting the words for which foreign students wrote annotations in their university text-books and the words that the students had found difficult during their reading.Xue and Nation(1984)combined the four earlier-compiled word lists(Campion and Elley’s,Pra-ninskas’s,Lynn’s,and Ghadessy’s)into the University Word List(UWL),consisting of about800words that were not in thefirst2000words of the GSL but that were of high fre-quency and of wide range in academic texts.Xue and Nation’s purpose of setting up the UWL was to create a list of high frequency words for learners with academic purposes, so that these words can be taught and directly studied in the same way as the words from the GSL.More recently,Coxhead(2000)developed the Academic Word List(AWL),using a corpus of3.5million running words,plus Range—the software which could calculate how often a word occurred(its frequency)and in how many different texts in the corpus it occurred(its range).The texts in her corpus were selected from different academic journals and university textbooks in four main areas:arts,commerce,law and natural science.The AWL contains570word families that account for approximately10%of the total words in her selected academic texts.Compared with the UWL,the AWL contains fewer word fam-ilies but provides more text coverage and more consistent word selection criteria.AWL now is a widely cited academic word list across a broad range of disciplines.

In addition to these discipline-crossing academic word lists,some researchers have focused on the academic vocabulary used in a single discipline.They assumed that there might be some unique features in the academic vocabulary across sub-disciplines of one discipline.Lam(2001)conducted an empirical study of academic vocabulary of Computer Science in order tofind the vocabulary problems encountered by the computer science stu-dents in reading academic texts.She noted that academic vocabulary was semantically dis-tinct from the same vocabulary when it appeared in general texts.She suggested that such lexical terms should be presented as a glossary of academic vocabulary with information of frequency of occurrences based on a specialized corpus.Mudraya(2006)established the Student Engineering English Corpus(SEEC),containing nearly2000000running words selected from engineering textbooks in13engineering disciplines and produced an aca-demic word list of1200word families for engineering students.The word families in her word list are frequently encountered in engineering textbooks compulsory for all engi-neering students,regardless of theirfields of specialization.She argued that academic vocabulary should be given more attention in the ESP classroom.J.Wang et al./English for Specific Purposes27(2008)442–458445 Despite the academic vocabulary lists across different disciplines compiled respectively by some researchers,there were few detailed studies exclusively on medical academic vocabulary used in thefield of medicine.Baker(1988)analyzed three rhetorical items in medical journal articles and she concluded that rhetorical items were in the category of academic vocabulary and that identifying academic items had some pedagogical implica-tions.Chen and Ge(2007)analyzed the occurrence and distribution of the AWL word families in medical RAs.Theirfindings confirmed that the academic vocabulary had a high text coverage and dispersion throughout a medical research article and served some important rhetorical functions,but they argued that the AWL was far from complete in representing the frequently used medical academic vocabulary in medical RAs and called for efforts in establishing a medical academic word list.

The study reported in this paper was designed to develop a Medical Academic Word List(MAWL)of the most frequently used medical academic vocabulary across different sub-disciplines in medical science.We hope the MAWL established in this study may serve as a guide for medical English instructors in curriculum preparation,especially in design-ing course-books of medical academic vocabulary,and for medical English learners in set-ting their vocabulary learning goals of reasonable size during a particular phase of English language learning.

2.Methodology

2.1.Corpus establishment

We established as the database for our study a written specialized corpus containing 1093011running words from288written texts of a single genre—medical research arti-cles,because reading and writing medical RAs is the fundamental concern for most learn-ers/users of English for Medical Purposes(EMP).

2.1.1.Data collection

All the written medical RAs to be adopted in the corpus were downloaded from the database ScienceDirect Online(http://www.sciencedirect.com),the world’s largest elec-tronic collection of science,technology and medicine with full text and bibliographic infor-mation,accessed at the library of the Fourth Military Medical University(FMMU).The database ScienceDirect Online contains over1800journals,including almost every top title across24disciplines from natural science to social science,and is considered to be one of the most authoritative and representative databases.

In the discipline of Medicine and Dentistry of ScienceDirect Online,there were32sub-ject areas at the time of our study,covering almost all thefields of medical science.The samples in the corpus were chosen from the following32subject areas.

1.Anesthesiology and Pain Medicine17.Medicine and Dentistry

18.Nephrology

2.Cardiology and Cardiovascular

Medicine

3.Clinical Neurology19.Obstetrics,Gynecology and Women’s

Health

Line missing

All the sample medical RAs included in the corpus were kept at their original length,written in the internationally conventionalized IMRD (Introduction–Method–Result–Dis-cussion)structure,published in the years 2000–2006and written by native English speak-ing writers by Wood’s (2001)‘‘strict ”criteria (first authors had to have names native to the country concerned and also be affiliated with an institution in countries where this lan-guage is spoken as the first language).

A three-round selection was conducted in choosing the sample medical RAs for the cor-pus.In the first round,we took each of the 32subject areas as one stratum and then by stratified random sampling we selected 3journals from each of the 32subject areas/stra-tum,totaling 96journals.In the second round,we randomly selected one issue out of each of the 96journals obtained in the first round.From the 96selected issues,the articles which were not following the IMRD format,were not written by native English speaking writers or were shorter than 2000running words or longer than 12000,running words were eliminated.In the third round,we selected 3criteria-fulfilling articles from each of the 96issues by simple random sampling.After this three-round selection,288texts were chosen for the corpus,the shortest one containing 2923running words and the longest one containing 10901running words (4939on average).

2.1.2.Data processing

In this study,data processing incorporated the standardization of the medical RAs to be stored in the corpus and the normalization of the words in the to-be-stored RAs.For the standardization of the medical RAs included in the corpus,the charts,diagrams,bib-liographies and some components in texts,which were not able to be processed by com-puter analyzing programs or should not be included in the lexical analysis in the chosen medical RAs,were removed so as to eliminate the factors unrelated to the lexical analysis and to ensure that the texts stored in the corpus be readable by the computer software.The

4.

Complementary and Alternative Medicine 20.Oncology 5.

Critical Care and Intensive Care Medicine 21.Ophthalmology 6.

Dentistry,Oral Surgery and Medicine 22.Orthopedics,Sports Medicine and Rehabilitation 7.

Dermatology 23.Otorhinolaryngology and Facial Plastic Surgery 8.

Emergency Medicine 24.Pathology and Medical Technology 9.

Endocrinology,Diabetes and Metabolism 25.Perinatology,Pediatrics and Child Health 10.

Forensic Medicine 26.Psychiatry and Mental Health 11.

Gastroenterology 27.Public Health and Health Policy 12.

Health Informatics 28.Pulmonary and Respiratory Medicine 13.

Hematology 29.Radiology and Imaging 14.

Hepatology 30.Surgery 15.

Immunology,Allergology and Rheumatology 31.Transplantation 16.Infectious Diseases 32.Urology

446J.Wang et al./English for Specific Purposes 27(2008)442–458

2.2.List development

2.2.1.Word selection criteria

The three principles(specialized occurrence,range and frequency of a word family) used by Coxhead in developing the AWL were adopted in our study with some adjust-ment.In her study,Coxhead named wide-range word families as the word families whose members occur in at least half of the28subject areas in her corpus.In this study,we also set50%as the criterion for inclusion.The members of a word family to be included in the MAWL should occur in16subject areas,half of the32subject areas in our corpus.The least frequency of the members of a word family to be included in the MAWL was30 times,a third of Coxhead’s100times,for the number of the running words(1000000) in our corpus was only about one third of that(3500000)in Coxhead’s corpus.

Coxhead(2000)also reported that in her AWL word selection,range was thefirst cri-terion and frequency the second because a word count based mainly on the frequency would have been biased by longer texts and topic-related words.This principle was also applied in the present study.Only word families covering16subject areas or more would be included in the MAWL,while word families occurring with very high frequency but covering fewer than16subject areas would be excluded.

In sum,all thefinally included word families in the MAWL met the following word selection criteria:

1.Specialized occurrence:The word families included had to be outside thefirst2000most

frequently occurring words of English,as represented by West’s GSL(1953).

2.Range:Members of a word family had to occur at least in16or more of the32subject

areas.

3.Frequency:Members of a word family had to occur at least30times in the corpus of

medical research articles.

As is known,the division between technical vocabulary and academic/sub-technical vocabulary is not always distinct(Chung&Nation,2003;Mudraya,2006).In some cases, arbitrary decisions need to be made to distinguish technical vocabulary and academic/sub-technical vocabulary.In compiling the MAWL,two experienced professors of English for Medical Purposes from our department were consulted whenever any arbitrary decision

was needed in the inclusion or the elimination of some criteria-fulfilling controversial word families in or from the computer-screened-out candidate list.

2.2.2.MAWL development

Following the standardization of the medical RAs and the normalization of the words, the frequency and the range of the word families in the corpus were counted and listed by computer software.The word selection criteria were then applied to locate our target word families to be included in the MAWL.The word families included in the GSL were elim-inatedfirst and then from the remaining word families,the word families occurring at least in16or more of the32subject areas were selected.From the screened-out word families, only those that occurred at least30times in the corpus of medical research articles were selected for the candidate word list.If there was any uncertainty about any of the crite-ria-fulfilling word families in the computer-screened-out candidate list,two experienced English professors who have taught and conducted studies on English for Medical Pur-poses for more than20years were consulted,as mentioned above,and they made the deci-sion on whether the word families in question should be included in or excluded from the finalized word list.Thefinalized list was termed as the Medical Academic Word List (MAWL).

3.Results

There were1093011running words,31275word families and4128pages of text in the corpus.Totally3345word families were found to have occurred P30times(frequency). After the elimination of the GSL word families(19word families),1446word families were left and650(44.95%)word families of them occurred in16or more subject areas under study(range).By consulting the two experienced professors of English for Medical Purposes,27(4.15%)borderline word families out of the650word families in the com-puter-screened candidate list were eliminated by expert opinion.Table1displays the27 word families which were eliminated by expert opinion.

Table1

Twenty-seven word families eliminated by expert opinion

Number Headword Frequency Range Number Headword Frequency Range 1pathogenesis1462215necrosis5516

2cytokine1191816cutaneous5516

3epithelial1151717stent5216

4mitochondrial1101618vivo5217

5carcinoma801619hepatic5119

6ligand791720aortic5018

7situ681621ischemia5017

8lymphoid681622cerebral4917

9vitro651723dorsal4616

10pulmonary651624hemorrhage4418

11posterior631825pathophysiology4417

12anterior631826exogenous3916

13lysis601627phenotypic3316

14cardia5618By our word selection criteria plus the expert opinion of our consulted experienced EMP professors,623(95.85%of650)word families were ultimately chosen and formed the Medical Academic Word List(see Appendix),which appeared133746times totally. In the MAWL,the most frequently used word was cell,which appeared4421times and appeared in all the32subject areas in the corpus,while the least frequently used one was static,which appeared30times and appeared in20subject areas in the corpus.Table 2shows the statistical results of the top30most frequently used word families in the MAWL.

The word families in the MAWL occurred in a wide range of the subject areas in our corpus.Of the623word families in the list,104(16.69%)covered all the32subject areas and321(51.52%)covered25or more subject areas(see Table3).Totally,486word fam-ilies(78.01%)in the MAWL occurred in20or more of the32subject areas under study. Taking the list as a whole,the frequency and the range of the word families included in the MAWL were positively correlated(r s=0.753,p=0.000).Among the top100most fre-quently used word families in the list,54(54%)appeared in all the32subject areas and Table2

Statistical results of the top30word families of the MAWL

Headword Frequency Range

Occurrence%Occurrence%

cell4421 3.3132100.00 data2226 1.6632100.00 muscular2049 1.532371.88 significant2039 1.5232100.00 clinic1598 1.1932100.00 analyze1447 1.0832100.00 respond1427 1.0732100.00 factor12370.9232100.00 method12090.9032100.00 protein11220.842887.50 tissue10970.822990.63 dose10350.772681.25 gene9990.752887.50 previous9260.6932100.00 demonstrate8610.32100.00 normal8190.6132100.00 process8190.6132100.00 similar8100.6132100.00 concentrate7870.592784.38 function7560.5732100.00 therapy7490.562990.63 indicate7450.5632100.00 area7340.5532100.00 obtain7050.5332100.00 research7040.5332100.00 vary6950.5232100.00 activate6730.503196.88 require6690.5032100.00 induce6680.503093.75 cancer6670.502268.75Table3

Subject-area coverage of word families in MAWL

Subject areas covered Number of word families% 3210416.69 3131 4.98 3030 4.82 2937 5.94 2832 5.14 2727 4.33 2629 4.65 2531 4.98 2438 6.10 2322 3.53 2232 5.13 2135 5.62 2038 6.10 1929 4.65 1840 6.42 1735 5.62 1633 5.30 Total623100.00 90(90%)appeared in25or more subject areas,while among the bottom100word families in the list,only1(1%)covered32subject areas and42(42%)covered fewer than20subject areas.

The average text coverage of the MAWL was12.24%of the total words in the medical RAs under study.The following passage randomly selected from a medical research article (Supp&Boyce,2005)in our corpus gave us a picture of the academic words used in such texts.The words included in the MAWL are underlined.

Chronic wounds represent a different kind of challenge for wound healing.These wounds do not usually involve a large surface area,but they have a high incidence in the general population and thus have enormous medical and economic impacts.

The most common chronic wounds include pressure ulcers and leg ulcers.In the Uni-ted States alone,these wounds are estimated to affect more than2million people with total clinical treatment costs as high as$1billion annually.Pressure ulcers, characterized by tissue ischemia and necrosis,are common among patients in long-term care settings,but patients hospitalized for short-term care settings are also at risk if mobility is impaired.Leg ulcers can have a variety of etiologies.Venous ulcers are the most common,often resulting from dysfunction of valves in veins of the lower leg that normally prevent the backflow of venous blood.Venous conges-tion leads to leakage of blood and macromolecules into the dermis,which can act as physical barriers to diffusion of oxygen and nutrients from the vasculature into the skin.Arterial insufficiency and diabetes also contribute to the development of leg ulcers.Arterial blockage can lead to tissue ischemia,inducing ulcers or necrosis.

The patients with diabetes are prone to leg ulcers because of several aspects of their disease,including neuropathy,poor circulation,and reduced response to infection.

Diabetic foot ulcers can lead to complications that result in as many as50,000ampu-tations annually in the United States,accounting for45–70%of all lower-extremityamputations performed.Historically,treatment of the relatively small chronic wounds has included the use of topical agents and occlusive dressings,and grafting of split-or full-thickness skin.Skin grafts can provide timely wound coverage,but may lead to painful donor sites which are slow to heal and may be unsuccessful because of underlying deficiencies in wound healing(p.403).

Among the305words in the above passage,37belonged to the MAWL.The MAWL text coverage in the passage was12.13%,which was consistent with the results of our study.

We have included only623base words of the word families in the MAWL,even though derivative forms are sometimes more frequent than the base forms,because in most cases learning the derived form requires very little extra work once the base form is known and if learners have control of basic word-building processes(Xue&Nation,1984).In the Appendix,the words in the MAWL are listed according to the frequency of their occur-rence in the corpus in a descending order,that is,the more frequently used word families are listed prior to those appearing less frequently in the corpus.This frequency priority in listing illustrates the relative usefulness of these words in medical English,which is one of the major objectives of the present study.

Only342(54.90%)of the623word families in the MAWL overlapped with the570 word families in the AWL.The marked difference between the MAWL and the AWL argues for itself that different practices and discourses of disciplinary communities require a more restricted discipline-based lexical repertoire,which undermines the usefulness of general academic word lists across different disciplines.Words like lesion and vein,though they tend to be considered as technical terms by people outside medicalfield,are included in the MAWL as medical academic vocabulary because they are general purpose medical words frequently used across different medical subject disciplines.Academic vocabulary or semi-technical vocabulary is a class of words between technical and non-technical words and usually with technical as well as non-technical implications.The word families included in the MAWL are medical academic vocabulary common across various sub-dis-ciplines of medicine but not within one single sub-discipline of medicine.

4.The pedagogical implications

The MAWL can serve as reference for a Medical English lexical syllabus.As the fre-quently and widely used medical academic vocabulary in medical RAs,the word families in the MAWL are worth special attention in designing some English for Medical Purposes (EMP)courses.The MAWL can provide some guidelines concerning vocabulary in curric-ulum preparation,particularly in designing EMP course-books for learning medical aca-demic vocabulary and in selecting relevant teaching/learning materials.The MAWL can help learners/instructors center on essential medical academic words,providing learners with some more specific approach to learning medical academic vocabulary and facilitat-ing instructors’setting of their medical academic vocabulary teaching goals in different stages.Well-timed and repeated exposure to the word families of the MAWL in a variety of contexts may significantly contribute to the acquisition of the deep-going properties of this important set of medical academic words.

The MAWL can also help learners study EMP academic vocabulary in a more con-scious and manageable way.The MAWL provides a clear and direct access to the mostfrequently used medical vocabulary for EMP learners and enables them to conduct explicit learning of vocabulary when these words arefirst introduced to the learners.With more exposure to medical texts,the learners will consolidate the vocabulary knowledge acquired from the MAWL.This pattern of learning academic vocabulary in medical context may also exemplify a compromise for a long-running debate about explicit learning versus guessing from context.

5.Conclusion

The MAWL,a medical academic word list based on a Medical RAs Corpus with 1093011running words,has been compiled for the better learning and application of med-ical academic words in the discipline of medicine.Although a number of word lists of aca-demic words in other disciplines have been reported,our MAWL has been so far the only list of academic words targeted exclusively on medical science.By developing a list of the frequently used medical academic words in medicine,we hope to inspire enough attention of instructors and learners/users to this type of vocabulary.It would be of special signif-icance for EMP students/instructors and medical professionals in learning or using med-ical academic vocabulary in medical reading and writing.

Our research is only a preliminary study on the medical academic vocabulary used in medical RAs.If possible,the MAWL needs to be rechecked in larger corpora or in other genres of medicine,such as medical textbooks or spoken medical academic English.We hope the availability of exercises and tests based on the MAWL will promote effective and efficient teaching and learning of medical academic vocabulary.

Appendix

Medical Academic Word List(submitted by frequency of word families)

Number Headword 1cell

2data

3muscular

4significant 5clinic

6analyze

7respond

8factor

9method

10protein

11tissue

12dose

13gene

14previous

15demonstrate Number Headword

16normal

17process

18similar

19concentrate

20function

21therapy

22indicate

23area

24obtain

25research

26vary

27activate

28require

29induce

30cancer

Number Headword

31occur

32role

33evident

34range

35identify

36period

37outcome

38phase

39specific

40liver

41infect

42culture

43mediate

44score

45affectNumber Headword 46potential

47individual 48expose

49involve

50survive

51target

52respective

53intervene

54site

55per

56design

57primary

58approach

59estimate

60component 61acid

62baseline

63procedure

overall

65pathway

66inflammation 67region

68participate 69lesion

70technique

71volume

72serum

73define

74evaluate

75prior

76assay

77injury

78section

79task

80achieve

81symptom

82detect

83molecular

84error

85incubate

86donor

87intense

88chronic Number Headword

fraction

90insulin

91contrast

92react

93source

94available

95disorder

96positive

97structure

98multiple

99generate

100conclude

101medium

102inhibit

103complex

104distribute

105major

106tumor

107initial

108channel

109receptor

110membrane

111stress

112strain

113nuclear

114ratio

115approximate

116release

117transplant

118surgery

119assess

120impact

121versus

122drug

123laboratory

124minimize

125onset

126reveal

127scan

128monitor

129criterion

130visual

131duration

Number Headword

132cycle

133investigate

134acute

135sequence

136select

137maximize

138whereas

139peak

140elevation

141image

142enzyme

143parameter

144isolate

145mutation

146enhance

147calcium

148glucose

149appropriate

150incidence

151conduct

152protocol

153background

154stimulate

155algorithm

156establish

157efficacy

158hypothesis

159feature

160interval

161mortality

162array

163derive

1series

165buffer

166specimen

167focus

168display

169plasma

170abstract

171grade

172secondary

173strategy

(continued on next page)

Appendix(continued)

Number Headword 174graft

175undergo 176peripheral 177transcription 178despite

179consist

180status

181furthermore 182immune 183reverse

184infuse

185author

186interact

187issue

188negative 1throughout 190goal

191vein

192chamber 193independent 194proliferation 195formation 196subsequent 197predict

198correspond 199correlate 200regulate

201exclude

202metabolic 203device

204recruit

205final

206impair

207inject

208percent

209publish

210remove

211syndrome 212exhibit

213blot

214defect

215biopsy

216index Number Headword

217diameter

218cognitive

219followup

220fluid

221lipid

222magnetic

223margin

224energy

225locate

226survey

227software

228profile

229attribute

230convention

231synthesis

232recover

233objective

234filter

235segment

236compound

237link

238guideline

239extract

240proportion

241regression

242questionnaire

243discharge

244respiratory

245gender

246summary

247promote

248tract

249toxic

250relevant

251episode

252acquire

253communicate

254internal

255dimension

256layer

257microscope

258adverse

259recipient

Number Headword

260density

261virus

262interpret

263document

2instruct

265oral

266theory

267illustrate

268probe

269diagnose

270consequence

271version

272create

273dilute

274skeletal

275novel

276threshold

277technology

278element

279dynamic

280challenge

281typical

282transfer

283aspect

284diet

285cohort

286external

287vector

288antibiotic

2domain

290temporary

291linear

292plus

293digit

294accurate

295concept

296transport

297rotate

298input

299absorb

300replicate

301distinct

302radicalNumber Headword 303superior 304contact

305ensure

306stable

307prevalence 308capture

309degrade

310anesthesia 311optimal

312kit

313bias

314proximal 315constant 316incorporate 317sufficient 318sustain

319label

320barrier

321zone

322chart

323implement 324trauma

325fund

326context

327hence

328community 329lateral

330facilitate 331trim

332prolong

333quantify 334perception 335accumulate 336expert

337grant

338amplification 339random

340construct 341mount

342renal

343environment 344couple Number Headword

345laser

346magnitude

347formula

348deficit

349alter

350access

351supplement

352eliminate

353graph

354shift

355capacity

356qualitative

357simulate

358globe

359modulate

360output

361attenuate

362statistic

363prescribe

3differentiate

365equivalent

366orient

367practitioner

368substantial

369chemical

370thereby

371consent

372intake

373stance

374trend

375overnight

376contribute

377enable

378spectrum

379assign

380option

381implicate

382aid

383tag

384portion

385electron

386cope

387decline

Number Headword

388species

3unique

390overlap

391adjacent

392node

393transform

394modify

395manual

396colleague

397core

398entry

399deficient

400cascade

401benefit

402identical

403parallel

404migrate

405reagent

406exceed

407comprise

408highlight

409evolution

410schedule

411organism

412predominant

413cumulative

414purchase

415plot

416seek

417emerge

418affinity

419valid

420code

421sterile

422compute

423prospect

424utilize

425deposit

426column

427contract

428scar

429axis

(continued on next page)Number Headword 430inferior

431deviate

432trigger

433loop

434precursor

435perceive

436preliminary 437undertake 438substitute

439whilst

440scenario

441adapt

442adult

443expand

444cord

445fundamental 446feedback

447sum

448elicit

449circulation 450tolerance

451team

452sex

453candidate

454assume

455imply

456terminal

457vascular

458hormone

459minor

460panel

461aggressive 462comprehensive 463residual

4perspective 465brief

466trace

467equip

468accelerate

469template

470mode

471diminish

472consecutive 473foundation Number Headword

474emphasize

475physiology

476oxide

477restore

478conflict

479phenomenon

480invade

481restrict

482attach

483longitude

484technical

485nevertheless

486append

487infiltrate

488bacterium

4agonist

490rely

491capable

492manipulate

493histology

494pharmacology

495saline

496persist

497integrity

498precede

499rear

500mental

501demographic

502pathology

503prominent

504apparatus

505paradigm

506adjust

507crucial

508nervous

509gradient

510disrupt

511encounter

512nitrogen

513format

514robust

515spontaneous

516principal

517transmit

Number Headword

518audit

519decade

520compromise

521cue

522gland

523assist

524inner

525intrinsic

526consume

527suppress

528fragment

529hypertension

530placebo

531dominant

532text

533susceptible

534spinal

535corporate

536principle

537relapse

538numerical

539resolve

540mature

541uniform

542diverse

543retain

544abdominal

545lane

546vital

547suspend

548voluntary

549diffuse

550rationale

551simultaneous

552transient

553secrete

554methanol

555confer

556constitute

557accomplish

558enroll

559embryo

560logistic

561project

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Wang Jing is an associate professor of English at the Department of Foreign Languages,Fourth Military Medical University,China.She has taught courses in college English and published articles on academic reading and on learning styles and communication strategies of Chinese learners.

Liang Shao-lan is an associate professor of English at the Department of Foreign Languages,Fourth Military Medical University,China.She has published articles on learning strategies of Chinese English learners and on genre analysis of English medical research articles.

Ge Guang-chun is a full professor of English and Chair at the Department of Foreign Languages,Fourth Military Medical University,China.He has taught and published extensively in applied linguistics and ESP and EMP in particular,where his areas of long-term interest include medical academic vocabulary,and genre and style analysis of medical research articles.