The impact of blinding on trial results: A systematic review and meta‐analysis
Bibliographic record
Abstract
Background: Blinding-the concealment of the arm to which participants have been randomized-is an important consideration for assessing the risk of bias of randomized trials. A growing body of evidence has, however, yielded inconsistent results on whether trials without blinding produce biased findings. Objective: To conduct a systematic review and meta-analysis of the evidence addressing whether trials with and without blinding produce different results. Methods: We searched MEDLINE, EMBASE, Cochrane Reviews, JBI EBP, and Web of Science, from inception to May 2022, for studies comparing the results of trials with and without blinding. Pairs of reviewers, working independently and in duplicate, reviewed search results for eligible studies and extracted data. We pooled the results of studies comparing trials with and without blinding of patients, healthcare providers/investigators, and outcome assessors/adjudicators using frequentist random-effects meta-analyses. We coded study results such that a ratio of odds ratio < 1 and difference in standardized mean difference < 0 indicate that trials without blinding overestimate the beneficial effects of treatments. Results: We identified 47 eligible studies. For dichotomous outcomes, we found low certainty evidence that trials without blinding of patients and healthcare providers and trials without blinding of patients may slightly overestimate the beneficial effects of treatments. We found moderate certainty evidence that trials without blinding of outcome assessors overestimate the beneficial effects of treatments. For continuous outcomes, we found low certainty evidence that trials without blinding of patients and healthcare providers may overestimate the beneficial effects of treatments. We found moderate certainty evidence that trials without blinding of outcome assessors/adjudicators probably overestimate the beneficial effects of treatments. Conclusion: Our systematic review and meta-analysis suggest that blinding may influence trial results in select situations-although the findings are of low certainty and the magnitude of effect is modest. In the absence of high-certainty evidence suggesting that trials with and without blinding produce similar results, investigators should be cautious about interpreting the results of trials without blinding.
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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 | 0.288 | 0.500 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.027 | 0.054 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".