Criteria to Evaluate the Quality of Outcome Reporting in Randomized Controlled Trials of Rehabilitation Interventions
Bibliographic record
Abstract
ABSTRACT: No standardized guideline for the reporting of outcomes measures in randomized controlled trials of rehabilitation interventions is currently available. This study includes four phases to identify, synthesize, and make recommendations for potential attributes of reporting criteria of outcome measures in rehabilitation randomized controlled trials. First, we surveyed the author instructions for rehabilitation journals to determine how journals require authors to report outcomes. Second, we reviewed all consolidated standards of reporting trials extensions to determine how other speciality groups require reporting of outcomes in randomized controlled trials. Third, we conducted a focused scoping review to examine the nature and variations of criteria used to evaluate the quality of outcome measures in randomized controlled trials. Finally, we synthesized the information from phases 1-3 and propose four criteria specific to the reporting of outcomes in randomized controlled trials of rehabilitation interventions: (1) clearly describe the construct to be measured as outcome(s); (2) justify the selection of outcome measures by mapping to World Health Organization International Classification of Function, Disability, and Health (International Classification of Functioning) framework; justify the psychometric properties (relevance, validity, reliability) of the selected measurement tool; (3) clearly describe the timing of outcome measurement, with consideration of the health condition, the course of disease, and hypothesized effect of intervention; and (4) complete and unselective reporting of outcome data.
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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 | 0.853 | 0.903 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.047 | 0.047 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.017 | 0.013 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
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".