High certainty evidence is stable and trustworthy, whereas evidence of moderate or lower certainty may be equally prone to being unstable
Bibliographic record
Abstract
OBJECTIVES: To assess to what extent the overall quality of evidence indicates changes to observe intervention effect estimates when new data become available. METHODS: We conducted a meta-epidemiological study. We obtained evidence from meta-analyses of randomized trials of Cochrane reviews addressing the same health-care question that was updated with inclusion of additional data between January 2016 and May 2021. We extracted the reported effect estimates with 95% confidence intervals (CIs) from meta-analyses and corresponding GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessments of any intervention comparison for the primary outcome in the first and the last updated review version. We considered the reported overall quality (certainty) of evidence (CoE) and specific evidence limitations (no, serious or very serious for risk of bias, imprecision, inconsistency, and/or indirectness). We assessed the change in pooled effect estimates between the original and updated evidence using the ratio of odds ratio (ROR), absolute ratio of odds ratio (aROR), ratio of standard errors (RoSE), direction of effects, and level of statistical significance. RESULTS: High CoE without limitations characterized 19.3% (n = 29) out of 150 included original Cochrane reviews. The update with additional data did not systematically change the effect estimates (mean ROR 1.00; 95% CI 0.99-1.02), which deviated 1.06-fold from the older estimates (median aROR; interquartile range [IQR]: 1.01-1.15), gained precision (median RoSE 0.87; IQR 0.76-1.00), and maintained the same direction with the same level of statistical significance in 93% (27 of 29) of cases. Lower CoE with limitations characterized 121 original reviews and graded as moderate CoE in 30.0% (45 of 150), low CoE in 32.0% (48 of 150), and very low CoE in 18.7% (28 of 150) reviews. Their update had larger absolute deviations (median aROR 1.12 to 1.33) and larger gains in precision (median RoSE 0.78-0.86) without clear and consistent differences between these categories of CoE. Changes in effect direction or statistical significance were also more common in the lower quality evidence, again with a similar extent across categories (without change in 75.6%, 64.6%, and 75.0% for moderate, low, very low CoE). As limitations increased, effect estimates deviated more (aROR 1.05 with zero, 1.11 with one, 1.25 with two, 1.24 with three limitations) and changes in direction or significance became more frequent (93.2% stable with no limitations, 74.5% with one, 68.2% with two, and 61.5% with three limitations). CONCLUSION: High-quality evidence without methodological deficiencies is trustworthy and stable, providing reliable intervention effect estimates when updated with new data. Evidence of moderate and lower quality may be equally prone to being unstable and cannot indicate if available effect estimates are true, exaggerated, or underestimated.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.359 | 0.736 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".