Active upper‐limb therapies for hand function, individual goal achievement, and self‐care in children with cerebral palsy: A network meta‐analysis
Bibliographic record
Abstract
AIM: To compare active upper-limb therapies for children with cerebral palsy using a network meta-analysis. METHOD: For this systematic review, five electronic databases were searched up to 2nd September 2024. Outcomes pertaining to improved hand use (Assisting Hand Assessment, AHA), goal attainment (Canadian Occupational Performance Measure, COPM), and self-care were analysed with therapies classified into 15 discrete categories. RESULTS: Quantitative analysis of 48 randomized controlled trials (n = 1629) was performed. Compared with control, treatment effect on hand function (AHA mean difference, standard error) was greater for bimanual therapy (BiM: 4.6, 1.0), modified constraint-induced movement therapy (mCIMT; 4.0, 1.0), goal-directed therapy (GDT; 3.8, 1.6), action observation (4.9, 1.1), and mCIMT + intensive (7.4, 2.5). For COPM performance, treatment effect was greater for cognitive orientation to occupational performance (CO-OP; 5.9, 1.4), BiM (3.3, 0.4), mCIMT (2.5, 0.5), GDT (2.3, 0.7), mirror therapy (2.6, 1.2), and mCIMT + GDT (4.2, 1.1). For self-care, treatment effect (standardized mean difference, standard error) was greater for BiM (0.39, 0.10), mCIMT (0.37, 0.08), and mCIMT + GDT (0.43, 0.32). INTERPRETATION: BiM and mCIMT were confirmed as effective interventions for hand function, self-care, and individual goal achievement. Mirror therapy, CO-OP, and four different combination approaches feature single studies, small sample sizes, and high risk of bias, requiring further clinical trials to confirm efficacy.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".