Development and validation of search filters to retrieve medication discontinuation articles in Medline and Embase
Bibliographic record
Abstract
BACKGROUND: Medication discontinuation studies explore the outcomes of stopping a medication compared to continuing it. Comprehensively identifying medication discontinuation articles in bibliographic databases remains challenging due to variability in terminology. OBJECTIVES: To develop and validate search filters to retrieve medication discontinuation articles in Medline and Embase. METHODS: We identified medication discontinuation articles in a convenience sample of systematic reviews. We used primary articles to create two reference sets for Medline and Embase, respectively. The reference sets were equally divided by randomization in development sets and validation sets. Terms relevant for discontinuation were identified by term frequency analysis in development sets and combined to develop two search filters that maximized relative recalls. The filters were validated against validation sets. Relative recalls were calculated with their 95% confidences intervals (95% CI). RESULTS: We included 316 articles for Medline and 407 articles for Embase, from 15 systematic reviews. The Medline optimized search filter combined 7 terms. The Embase optimized search filter combined 8 terms. The relative recalls were respectively 92% (95% CI: 87-96) and 91% (95% CI: 86-94). CONCLUSIONS: We developed two search filters for retrieving medication discontinuation articles in Medline and Embase. Further research is needed to estimate precision and specificity of the filters.
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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 | 0.249 | 0.608 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.067 | 0.031 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".