Validation of five search filters for retrieval of clinical practice guidelines produced low precision
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to validate search filters for retrieval of clinical practice guidelines (CPGs) in MEDLINE, Embase, and PubMed. STUDY DESIGN AND SETTING: A search for filters for identifying CPGs was conducted in Google and the InterTASC Information Specialists Sub-Group Search Filter Resource. To retrieve a random sample of CPGs to test sensitivity and precision of the filters, we used the TRIP and Epistemonikos databases. The citations were screened independently by two researchers. The sensitivity and precision were calculated. RESULTS: Five search filters were retrieved: two from the Canadian Agency for Drugs and Technologies in Health (CADTH), two from the University of Texas, and one from the MD Anderson Cancer Center Library. A total of 478 records were screened to identify 109 CPGs, which comprised the sample for testing sensitivity and precision. The sensitivity ranged from 87% to 98% for the five search filters and very low precision (<1%) across all databases. CONCLUSION: Knowledge users who are interested in retrieving all relevant CPGs can use the CADTH broad filter with the highest sensitivity. However, our analysis shows that it remains difficult to efficiently identify CPGs because of low precision of five search filters. We recommend searching guideline-specific resources as a more time-efficient approach than searching bibliographic databases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.152 | 0.618 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".