1. Identification of clinically important PECODR elements within medical journal abstracts Patient-population-problem, Exposure-intervention, Comparison, Outcome, Duration, & Results Martin Dawes, Pierre Pluye, Laura Shea, Roland Grad, Arlene Greenberg, Jian-Yun Nie

2. Evidence-Based Practice  See a patient  Have a question  Sometimes seek an answer  Firstly in secondary sources  E-B synopses eg GAC guidelines  Secondly in primary sources  Medline

3. Questions Unstructured = Unanswerable Make them structured  PDo women > 50 years  Etaking calcium  Ccompared to women taking nothing  Ohave fewer hip fractures  Dover 5 years  RNNT of 30

4. PROBLEMS - SOLUTIONS - QUESTION Problems  Too much information  Information keeps changing Solutions  Filters for evidence  MESH & PubMed clinical queries Questions 1.Would PECODR indexing the clinical literature help me find information for my patient? 2.Can we identify PECODR elements in abstracts? 3.Would the words or groups of words used help us to automatically do this?

5. METHODS Pilot study Convenience sample (N = 40)  20 research articles (Evidence Based Medicine synopses )  20 corresponding abstracts (PubMed) Converted the information into a text file Analyse the content: Qualitative and Quantitative

6. Methods  6 themes defined: P, E, C, O, D, R  Extract text and allocate to a themes  extracts made from a word or words,  made up from a series of characters (the gold standard denominator)  Independently by two researchers  10 rules developed iteratively eg exclusive assignment « 1 extract  1 theme »

7. Coding Despite major differences Despite major differences in blood pressure lowering, in blood pressure lowering, there were no there were no outcome outcome differences differences between atenolol between atenolol and placebo in the four studies, and placebo in the four studies, comprising 6825 patients, comprising 6825 patients, who were followed up for a mean of 4·6 years who were followed up for a mean of 4·6 years Should this be coded under C?

8. Quantitative Analysis Characters assigned to PubMed Abstracts # % EBM Synopses # % P5,45017,0%13,10431,8% E7,35422,9%8,16819,8% C3,42610,7%4,41710,7% O6,64920,7%8,75121,2% D1,0583,3%1,4733,6% R8,01025,0%5,35013,0% Total3194741263

9. Quantitative Analysis 6 « PECODR » themes 40 documents 73,315 characters 20 Abstracts Characters #% 20 SYNOPSES Characters #% Agreement 27,36085,4%35,71686,6% Consensus 3,80811,9%4,87311,8% Arbitration 8842,8%6741,6% Disagreement 1050,3%00,0% So the method of coding seems to work

10. Quantitative Analysis 1. For each theme PECODR Identification (inductive) of potential patterns or series of words Compare theme patterns comparison, compare, compared, comparing, etc, 2. For each pattern Only for those with a pattern frequency of > 70% found in the themes

11. Results: PECODR by abstracts & synopses docsextracts docsextracts P 19 89 20116 E 20163 20180 C 18 92 19120 O 20169 20187 D 15 36 18 45 R 20 210 20187 Total759835 PECODR exists in abstracts

12. RESULTS P = Patient-population-problem No pattern: Example, diabetic E = Exposure-intervention No pattern: Example, Amoxil

13. RESULTS C = Comparison: C = Comparison: number of extracts assigned to C with C pattern and % of total C extracts found not assigned to C C PatternsABSTRACTS # extracts C (92) % C SYNOPSES # extracts C (120) (120) % C  Comparing 2100%450%  Compared 1983%2086%  Placebo 4783%2084%  Standard 380%475%  Versus 271%5100%  Than 4267%1881% All were in C extracts 4 more occurrences of the word “comparing” were in parts of the text that were not C extracts

14. RESULTS O Patterns ABSTRACTS (169) ABSTRACTS (169) # extracts O % O SYNOPSES (187) # extracts O % C  End point 3100%0NA  Mortality 3585%5093%  Death 583%889%  Incidence 1275%583%  Outcome 1470%3476% O = « Outcome »: O = « Outcome »: number of extracts assigned to O with O pattern and % of total O extracts found not assigned to O

15. RESULTS R = Results: R = Results: number of extracts assigned to R with R pattern and % of total R extracts found not assigned to R R patternsABSTRACTS(210) # extracts R % R SYNOPSES(187) # extracts R % R  Frequent 1100%0NA  Correlated 1100%2  Strongly 2100%0NA  Closely 1100%0NA  Cast doubt 1100%0NA

16. RESULTS  challenge 1100%0NA  superiority 1100%0NA  replicate 1100%0NA  chance 1100%0NA  gradient 1100%0NA  fewer 3100%788%  better 3100%571%  likely 1100%267%  decrease 3100%250%  differ 1192%2483%  confidence interval 990%0NA  increase 889%1376%  significant 2586%583%  difference 686%675%  odds ratio 583%1100%  occur 480%150%  associated 778%583%  greater 375%3100%  higher 571%267%  ruling 0NA1100%  highest 0NA1100%  lowest 0NA1100% R R (more) Significance

17. Results Summary Presence of text or text patterns  PECODR elements are present in the majority of abstracts with the exception of Duration (75%) Patterns of PECODR  P & E = Medical or Drug Terms (thesaurus)  The other element related extracts contain some patterns

18. DISCUSSION Limits  Pilot Study (N=40)  Exploratory study (rules developed iteratively for text extraction/ qualitative pattern identification) Further Research  Textual Analysis using clever software with UdM to better identify patterns  Larger sample of papers  Do GP’s want a PECODR search engine?

19. CONCLUSION 1.Possible standard textual patterns (lexico- semantics) may help automatically determine PECODR elements 2.If this is possible we may be able to develop a prototype of new technology for automatic information retrieval (aspectual Search Engine) 3.To index the clinical information according to needs' for the users (eg family practitioners) 4.That will help people answer clinical questions…maybe

20. Martin Dawes DIABETIC Therapy 28,000 Prognosis 2,000 Harm 1,780 Aetiology 4000 Oral Hypoglycaemics 1,780 Heart attacks 2,300 Problem/Patient Select Exposure Select Outcome Number under each heading represents articles Cost Effectiveness 1,300 Diagnosis12,000 Insulin 12,000 Antihypertensive 6,000 Mortality 60 >3 years n=20 Select Duration Male/Female Old/Adult/Young 1.NNT 25 4.6 years 2.NNT 34 4.2 years 3.N/A 4.NNT 54 1.8 years 5.NNT 35 N/A