1 / 21

Martin Dawes, Pierre Pluye, Laura Shea, Roland Grad, Arlene Greenberg, Jian-Yun Nie

Identification of clinically important PECODR elements within medical journal abstracts P atient-population-problem, E xposure-intervention, C omparison, O utcome, D uration, & R esults. Martin Dawes, Pierre Pluye, Laura Shea, Roland Grad, Arlene Greenberg, Jian-Yun Nie.

yamka
Download Presentation

Martin Dawes, Pierre Pluye, Laura Shea, Roland Grad, Arlene Greenberg, Jian-Yun Nie

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Identification of clinically important PECODR elements within medical journal abstractsPatient-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 • P Do women > 50 years • E taking calcium • C compared to women taking nothing • O have fewer hip fractures • D over 5 years • R NNT of 30

  4. PROBLEMS - SOLUTIONS - QUESTION Problems • Too much information • Information keeps changing Solutions • Filters for evidence • MESH & PubMed clinical queries Questions • Would PECODR indexing the clinical literature help me find information for my patient? • Can we identify PECODR elements in abstracts? • 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 <R> Despite major differences <O> in blood pressure lowering, <R> there were no <O> outcome <R> differences <E> between atenolol <C> and placebo in the four studies, <P> comprising 6825 patients, <D> who were followed up for a mean of 4·6 years Should this be coded under C?

  8. Quantitative Analysis

  9. Quantitative Analysis So the method of coding seems to work 6 « PECODR » themes

  10. QuantitativeAnalysis 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 PECODR exists in abstracts docs extracts docs extracts P 19 89 20 116 E 20 163 20 180 C 18 92 19 120 O 20 169 20 187 D 15 36 18 45 R 20 210 20 187 Total 759 835

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

  13. 4 more occurrences of the word “comparing” were in parts of the text that were not C extracts All were in C extracts RESULTS • C = Comparison: number of extracts assigned to C with C pattern and % of total C extracts found not assigned to C

  14. RESULTS • 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: number of extracts assigned to R with R pattern and % of total R extracts found not assigned to R

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

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

More Related