Optimal search filters for detecting quality improvement studies in Medline
Bibliographic record
Abstract
BACKGROUND: As the knowledge translation and comparative effectiveness research agendas gain momentum, we can expect more evidence on which to base quality improvement (QI) programmes. Unaided searches for such content in the literature, however, are likely to be daunting, with searches missing key articles while mainly retrieving articles that are irrelevant to the question being asked. The objective of this study was to develop and validate optimal Medline search filters for retrieving original and review articles about clinical QI. METHODS: Analytical survey in the McMaster Clinical Hedges database and Health Knowledge Refinery (HKR) of 161 clinical journals to determine the operating characteristics of QI search filters developed by computerised combinations of terms selected to detect original QI studies and systematic reviews meeting basic methodological criteria for scientific merit. Results from a derivation random subset of articles were tested in a validation random subset. RESULTS: The Clinical Hedges QI database contained 49,233 citations of which 471 (0.96%) were original or review QI studies; of those, 282 (60%) were methodologically sound. Combinations of search terms reached peak sensitivities of 100% at a specificity of 89.3% for detecting methodologically sound original and review QI studies, and sensitivities of 97.6% at a specificity of 53.0% for detecting all original and review QI studies independent of rigour. Operating characteristics of the search filters derived in the development database worked similarly in the validation database, without statistical differences. CONCLUSION: New empirically derived Medline search filters have been validated to optimise retrieval of original and review QI articles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.480 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".