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Record W4323536717 · doi:10.1101/2023.03.05.23286821

The impact of blinding on trial results: A systematic review and meta-analysis

2023· review· en· W4323536717 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuemedRxiv · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsImpactDalhousie UniversityMcMaster University
Fundersnot available
KeywordsBlindingMeta-analysisPublication biasMedicineRandomized controlled trialSystematic reviewMEDLINEOdds ratioStrictly standardized mean differenceClinical trialInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Blinding—the concealment of the arm to which participants have been randomized—is an important consideration for assessing 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 (ROR) < 1 and difference in standardized mean difference (dSMD) < 0 indicate that trials without blinding overestimate treatment effects. Results We identified 47 eligible studies. For dichotomous outcomes, we found low certainty evidence that trials without blinding of patients and healthcare providers, outcome assessors/adjudicators, and patients may slightly overestimate treatment effects. For continuous outcomes, we found low certainty evidence that trials without blinding of outcome assessors/adjudicators and patients may slightly overestimate treatment effects. Conclusion Our systematic review and meta-analysis suggests that blinding may influence trial results in select situations—albeit 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
gptMetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad)
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.408
metaresearch head score (Gemma)0.266
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (broad), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4080.266
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0790.069
Bibliometrics0.0020.013
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0050.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.907
GPT teacher head0.632
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it