Precision of healthcare systematic review searches in a cross‐sectional sample
Bibliographic record
Abstract
BACKGROUND: In systematic reviews, search precision is generally traded off against the desire to retrieve all relevant studies; however, there is no published evidence on typical precision values. The objective of this study is to establish typical values for the precision of systematic review searches in healthcare. METHODS: From an existing cross-sectional sample of 300 MEDLINE-indexed systematic reviews, those that reported the flow of bibliographic records through the review process (n = 109) were examined. Where the ratio of the number of included studies and the number of unique retrievals could be determined, overall and median precision of the search was calculated. Subgroup analyses were conducted by review type (treatment/prevention, diagnosis/prognosis, epidemiology, other), eligible study designs, number of databases searched and for updates of existing systematic reviews. RESULTS: Precision could be calculated for 94 systematic reviews. The median [interquartile range] precision was 0.029 [0.013, 0.081] with a range of 0.007-0.358. In this sample, precision did not differ significantly in any of the subgroups examined. IMPLICATIONS: Search precision of approximately 3% was typical in this cross-section of health related systematic reviews. This finding is useful for systematic review teams to gauge review resource needs and for information specialists in evaluating their searches. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.619 | 0.886 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".