Systematic Review and Meta-Analysis of Constraint-Induced Movement Therapy in the Hemiparetic Upper Extremity More Than Six Months Post Stroke
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review and meta-analysis of the available evidence on the effectiveness of constraint-induced movement therapy (CIMT) in the hemiparetic upper extremity (UE) among individuals who were more than 6 months post stroke. METHODS: A literature search of multiple databases (PubMed, CINAHL, and EMBASE) was conducted to identify articles published in the English language up to and including July 2012. Studies were included for review if (1) ≥50% of the sample had sustained a stroke, (2) the research design was a randomized controlled trial (RCT), (3) the mean time since stroke was ≥6 months for both the treated and control groups, (4) the treatment group received CIMT, (5) the control group received a form of traditional rehabilitation, and (6) functional improvement was assessed both pre and posttreatment. Methodological quality was assessed using the PEDro tool with a score out of 10. RESULTS: Sixteen RCTs (PEDro scores 4-8) met inclusion criteria and included a pooled sample size of 572 individuals with a mean age of 58.2 years (range, 30-87). The meta-analysis revealed a significant treatment effect on the amount of use and quality of movements subscales of the Motor Activity Log ( P < .001, for both), Fugl-Meyer Assessment ( P = .014), and Action Research Arm Test ( P = .001); however, there was no significant treatment effect demonstrated by the Wolf Motor Function Test ( P = .120) or FIM ( P = .070). CONCLUSIONS: CIMT to improve UE function is an appropriate and beneficial therapy for individuals who have sustained a stroke more than 6 months previously.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".