Time to Update and Quantitative Changes in the Results of Cochrane Pregnancy and Childbirth Reviews
Bibliographic record
Abstract
BACKGROUND: The recommended interval between updates for systematic reviews included in The Cochrane Library is 2 years. However, it is unclear whether this interval is always appropriate. Whereas excessive updating wastes time and resources, insufficient updating allows out-of-date or incomplete evidence to guide clinical decision-making. We set out to determine, for Cochrane pregnancy and childbirth reviews, the frequency of updates, factors associated with updating, and whether updating frequency was appropriate. METHODOLOGY/PRINCIPAL FINDINGS: Cochrane pregnancy and childbirth reviews published in Issue 3, 2007 of the Cochrane Database of Systematic Reviews were retrieved, and data were collected from their original and updated versions. Quantitative changes were determined for one of the primary outcomes (mortality, or the outcome of greatest clinical significance). Potential factors associated with time to update were assessed using the Cox proportional hazard model. Among the 101 reviews in our final sample, the median time before the first update was 3.3 years (95% CI 2.7-3.8). Only 32.7% had been updated within the recommended interval of 2 years. In 75.3% (76/101), a median of 3 new trials with a median of 576 additional participants were included in the updated versions. There were quantitative changes in 71% of the reviews that included new trials (54/76): the median change in effect size was 18.2%, and the median change in 95% CI width was 30.8%. Statistical significance changed in 18.5% (10/54) of these reviews, but conclusions were revised in only 3.7% (2/54). A shorter time to update was associated with the same original review team at updating. CONCLUSIONS/SIGNIFICANCE: Most reviews were updated less frequently than recommended by Cochrane policy, but few updates had revised conclusions. Prescribed time to update should be reconsidered to support improved decision-making while making efficient use of limited resources.
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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 | 0.497 | 0.906 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".