The identification of clinically important elements within medical journal abstracts: Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR).
BACKGROUND
Information retrieval in primary care is becoming more difficult as the volume of medical information held in electronic databases expands. The lexical structure of this information might permit automatic indexing and improved retrieval.


OBJECTIVE
To determine the possibility of identifying the key elements of clinical studies, namely Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR), from abstracts of medical journals.


METHODS
We used a convenience sample of 20 synopses from the journal Evidence-Based Medicine (EBM) and their matching original journal article abstracts obtained from PubMed. Three independent primary care professionals identified PECODR-related extracts of text. Rules were developed to define each PECODR element and the selection process of characters, words, phrases and sentences. From the extracts of text related to PECODR elements, potential lexical patterns that might help identify those elements were proposed and assessed using NVivo software.


RESULTS
A total of 835 PECODR-related text extracts containing 41,263 individual text characters were identified from 20 EBM journal synopses. There were 759 extracts in the corresponding PubMed abstracts containing 31,947 characters. PECODR elements were found in nearly all abstracts and synopses with the exception of duration. There was agreement on 86.6% of the extracts from the 20 EBM synopses and 85.0% on the corresponding PubMed abstracts. After consensus this rose to 98.4% and 96.9% respectively. We found potential text patterns in the Comparison, Outcome and Results elements of both EBM synopses and PubMed abstracts. Some phrases and words are used frequently and are specific for these elements in both synopses and abstracts.


CONCLUSIONS
Results suggest a PECODR-related structure exists in medical abstracts and that there might be lexical patterns specific to these elements. More sophisticated computer-assisted lexical-semantic analysis might refine these results, and pave the way to automating PECODR indexing, and improve information retrieval in primary care.
Dawes
Martin
M
Department of Family Medicine, McGill University, Nd Health Sciences Library, Sir Mortimer B Davis-Jewish General Hospital, Montréal, Québec, Canada. martin.dawes@mcgill.ca
Pluye
Pierre
P
Shea
Laura
L
Grad
Roland
R
Greenberg
Arlene
A
Nie
Jian-Yun
JY
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D015706:Bibliometrics; D002986:Clinical Trials as Topic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008492:Medical Informatics Computing; D009317:National Library of Medicine (U.S.); D010506:Periodicals as Topic; D039781:PubMed / Q000458:organization & administration; D009626:Terminology as Topic; D014481:United States