Searching two or more databases decreased the risk of missing relevant studies: a metaresearch study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Assessing changes in coverage, recall, review, conclusions and references not found when searching fewer databases. METHODS: In randomly selected 60 Cochrane reviews, we checked included study publications' coverage (indexation) and recall (findability) using different search approaches with MEDLINE, Embase, and CENTRAL and related them to authors' conclusions and certainty. We assessed characteristics of unfound references. RESULTS: Overall 1989/2080 included references, were indexed in ≥1 database (coverage = 96%). In reviews where using one of our search approaches would not change conclusions and certainty (n = 44-54), median coverage and recall were highest (range 87.9%-100.0% and 78.2%-93.3%, respectively). Here, searching ≥2 databases reached >95% coverage and ≥87.9% recall. In reviews with unchanged conclusions but less certainty (n = 2-8): 63.3%-79.3% coverage and 45.0%-75.0% recall. In reviews with opposite conclusions (n = 1-3): 63.3%-96.6% and 52.1%-78.7%. In reviews where a conclusion was no longer possible (n = 3-7): 60.6%-86.0% and 20.0%-53.8%. The 265 references that were indexed but unfound were more often abstractless (30% vs. 11%) and older (28% vs. 17% published before 1991) than found references. CONCLUSION: Searching ≥2 databases improves coverage and recall and decreases the risk of missing eligible studies. If researchers suspect that relevant articles are difficult to find, supplementary search methods should be used.
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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 | 0.882 | 0.953 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".