Blinding in Physical Therapy Trials and Its Association with Treatment Effects
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine whether blinding of participants, assessors, health providers, and statisticians have an effect on treatment effect estimates in physical therapy (PT) trials. DESIGN: This was a meta-epidemiological study. Randomized controlled trials in PT were identified by searching the Cochrane Database of Systematic Reviews for meta-analyses of PT interventions. Assessments of blinding in PT trials were conducted independently following established guidelines. RESULTS: Three hundred ninety-three trials and 43 meta-analyses that included 44,622 patients contributed to this study. Only a quarter of the trials were adequately blinded (n = 80; 20%). Most individual components of blinding as well as what they were blinded to were also poorly reported. Although trials with inappropriate blinding of assessors and participants tended to underestimate treatment effects when compared with trials with appropriate blinding of assessors and participants, the difference was not statistically significant (effect size, -0.07; 95% confidence interval, -0.22 to 0.08; effect size, -0.12; 95% confidence interval, -0.30 to 0.06, respectively). CONCLUSIONS: The lack of statistical significance between blinding and effect sizes should not be interpreted as meaning that an impact of blinding on effect size is not present in PT. More empirical evidence in a larger sample is needed to determine which biases are likely to influence reported effect sizes of PT trials and under which conditions.
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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 | 0.531 | 0.760 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".