An alternative to the hand searching gold standard: validating methodological search filters using relative recall
Bibliographic record
Abstract
BACKGROUND: Search filters or hedges play an important role in evidence-based medicine but their development depends on the availability of a "gold standard"--a reference standard against which to establish the performance of the filter. We demonstrate the feasibility of using relative recall of included studies from multiple systematic reviews to validate methodological search filters as an alternative to validation against a gold standard formed through hand searching. METHODS: We identified 105 Cochrane reviews that used the Highly Sensitive Search Strategy (HSSS), included randomized or quasi-randomized controlled trials, and reported their included studies. We measured the ability of two published and one novel variant of the HSSS to retrieve the MEDLINE-index studies included in these reviews. RESULTS: The systematic reviews were comprehensive in their searches. 72% of included primary studies were indexed in MEDLINE. Relative recall of the three strategies ranged from .98 to .91 across all reviews and more comprehensive strategies showed higher recall. CONCLUSION: An approach using relative recall instead of a hand searching gold standard proved feasible and produced recall figures that were congruent with previously published figures for the HSSS. This technique would permit validation of a methodological filter using a collection of approximately 100 studies of the chosen design drawn from the included studies of multiple systematic reviews that used comprehensive search strategies.
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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 | 0.695 | 0.888 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.030 | 0.021 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".