Blinding in Rehabilitation Research Empirical Evidence on the Association Between Blinding and Treatment Effect Estimates
Bibliographic record
Abstract
Objective The aim of the study was to assess the association between different types of blinding on treatment effects estimates in the area of rehabilitation. Methods Evidence synthesis was used for the design of the study. This study included any systematic review or meta-epidemiological study that investigated associations between any blinding component and treatment effects estimates in randomized control trials in the area of rehabilitation. The information obtained from the included studies was organized by type of blinding and summarized using a narrative and/or quantitative approach when possible. If there were enough data of estimates for any type of blinding, we decided to pool them in an exploratory fashion. Results The literature search identified a total of 1015 citations, of which 7 studies fulfilled the inclusion criteria. Studies show overestimation, underestimation, or neutral associations for different types of blinding on treatment effects. Conclusions Associations were mixed and did not follow a consistent pattern. Lack/poor reporting of blinding, small sample sizes, and heterogeneity of data sets could have led to nonsignificant and inconsistent results obtained by the included studies. Although the evidence regarding the association between blinding and treatment effect estimates is still inconclusive in the rehabilitation field, based on the available literature, researchers should select creative solutions to avoid performance and detection bias.
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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 | 0.560 | 0.824 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".