Outcome Assessment in Randomized Controlled Trials of Stroke Rehabilitation
Bibliographic record
Abstract
The lack of a unified approach to outcome assessment in stroke rehabilitation limits our ability to interpret evidence provided by randomized controlled trials (RCTs). The purpose of this review was to identify outcomes and assessment tools reported in RCTs of stroke rehabilitation interventions as a first step toward consistent assessment of outcomes. Given that the validity of research is linked to reliability and validity of measurement, the relationship between the use of previously developed outcome measures and the methodological quality of RCTs was explored. Electronic literature searches identified RCTs examining stroke rehabilitation therapies from 1968 to 2005. The Physiotherapy Evidence Database (PEDro) scale was used to assess methodological quality. Cited outcomes were recorded and assessment tools identified as previously published or study specific. Four hundred ninety-one RCTs cited the assessment of 1447 outcomes using 489 measurement tools. Two hundred fifty-four of these were previously published, and 235 were study specific. A core of 30 frequently cited tools was identified. The use of previously published assessment tools to evaluate primary study outcomes was associated with higher PEDro scores. Significant heterogeneity in outcome assessment was demonstrated, although a core of 30 frequently cited tools could be identified. Appropriate evaluation and selection of outcome measures would enhance the methodological quality of randomized controlled trials.
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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 | 0.279 | 0.578 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.017 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".