Precision of healthcare systematic review searches in a cross‐sectional sample
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.747 | 0.749 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it