Constraint-Induced Movement Therapy Compared to Dose-Matched Interventions for Upper-Limb Dysfunction in Adult Survivors of Stroke: A Systematic Review with Meta-analysis
Bibliographic record
Abstract
PURPOSE: To summarize the existing literature examining constraint-induced movement therapy (CIMT), relative to dose-matched control interventions, for upper-limb (UL) dysfunction in adult survivors of stroke. METHODS: CINAHL, Cochrane Library, Embase, NARIC/CIRRIE-Rehabdata, PEDro, PubMed, Scopus, and Web of Science were searched from their inception to February 2011. Trial quality was described using the PEDro scale. The findings were summarized with meta-analysis. RESULTS: For the 22 trials identified, the mean (SD) PEDro score was 6.4 (1.2). Meta-analysis showed CIMT to be superior to dose-matched interventions based on indicators of UL motor capacity (15 trials, n=432; standardized mean difference [SMD]=0.47, 95% CI, 0.27-0.66) and UL ability (14 trials, n=352; SMD=0.80, 95% CI, 0.57-1.02); Functional Independence Measure scores (6 trials, n=182; mean difference [MD]=5.05, 95% CI, 2.23-7.87); and Motor Activity Log scores (Amount of Use: 12 trials, n=318; MD=1.05, 95% CI, 0.85-1.24; Quality of Movement: 11 trials, n=330; MD=0.89, 95% CI, 0.69-1.08). CONCLUSIONS: Compared to control interventions of equal duration and dose, CIMT produced greater improvements in a variety of indicators of UL function in adult survivors of a stroke with residual movement of their upper limb.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".