How Quickly Do Systematic Reviews Go Out of Date? A Survival Analysis
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are often advocated as the best source of evidence to guide clinical decisions and health care policy, yet we know little about the extent to which they require updating. OBJECTIVE: To estimate the average time to changes in evidence that are sufficiently important to warrant updating systematic reviews. DESIGN: Survival analysis of 100 quantitative systematic reviews. SAMPLE: Systematic reviews published from 1995 to 2005 and indexed in ACP Journal Club. Eligible reviews evaluated a specific drug or class of drug, device, or procedure and included only randomized or quasi-randomized, controlled trials. MEASUREMENTS: Quantitative signals for updating were changes in statistical significance or relative changes in effect magnitude of at least 50% involving 1 of the primary outcomes of the original systematic review or any mortality outcome. Qualitative signals included substantial differences in characterizations of effectiveness, new information about harm, and caveats about the previously reported findings that would affect clinical decision making. RESULTS: The cohort of 100 systematic reviews included a median of 13 studies and 2663 participants per review. A qualitative or quantitative signal for updating occurred for 57% of reviews (95% CI, 47% to 67%). Median duration of survival free of a signal for updating was 5.5 years (CI, 4.6 to 7.6 years). However, a signal occurred within 2 years for 23% of reviews and within 1 year for 15%. In 7%, a signal had already occurred at the time of publication. Only 4% of reviews had a signal within 1 year of the end of the reported search period; 11% had a signal within 2 years of the search. Shorter survival was associated with cardiovascular topics (hazard ratio, 2.70 [CI, 1.36 to 5.34]) and heterogeneity in the original review (hazard ratio, 2.15 [CI, 1.12 to 4.11]). LIMITATION: Judgments of the need for updating were made without involving content experts. CONCLUSION: In a cohort of high-quality systematic reviews directly relevant to clinical practice, signals for updating occurred frequently and within a relatively short time.
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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 | 0.539 | 0.898 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.028 | 0.035 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.015 | 0.036 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".