Blinded or Nonblinded Randomized Controlled Trials in Rehabilitation Research A Conceptual Analysis Based on a Systematic Review
Bibliographic record
Abstract
Objective Some recent studies suggest that double blinding should not be considered a validity criterion in randomized controlled trials (RCTs) on real-life circumstances. This study aims to assess whether blinding vs. nonblinding have been analyzed conceptually in the rehabilitation literature. Propositions on the role of blinding in RCTs on rehabilitation are presented based on the conceptual analysis. Design Study questions, literature search strategy, and inclusion and exclusion criteria for the original studies were formulated. A health science librarian carried out the literature search. Eligibility was assessed and data extraction was performed by two independent researchers. Results The literature search identified a total of 1052 citations, of which 13 studies fulfilled the inclusion criteria. None of the included studies answered our research questions, and thus we were unable to extract any relevant data. Conclusions The ideas on blinding vs. nonblinding in RCTs have not been considered in the rehabilitation research literature. This conceptual systematic review proposes that a physical therapy modality is a single core element, and when the study question is on effectiveness of this single core element itself, double blinding in an RCT is indicated. In all other RCTs in rehabilitation, double blinding is not indicated and double blinding should not be considered a criterion for the assessment of risk of 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 | 0.570 | 0.655 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.032 | 0.020 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| 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".