Predictors of upper‐extremity motor outcomes after constraint‐induced therapy or bimanual training in children with cerebral palsy: A systematic review
Bibliographic record
Abstract
AIM: To systematically review the factors that predict changes in upper-extremity motor function in response to constraint-induced movement therapy (CIMT) or bimanual training in children with cerebral palsy (CP). METHOD: The PubMed, Cochrane, Embase, Scopus, CINAHL, and PEDro databases were searched. Studies were included if predictor variables were measured at baseline and were linked to an upper-extremity motor function outcome after intervention. Studies investigating only neurophysiological biomarkers were excluded. Two independent reviewers conducted study selection, risk of bias assessment, data extraction, and synthesis. RESULTS: The electronic search yielded 4317 articles, of which 23 were included (14 of 23 were randomized controlled trials). The most frequently studied factors were baseline motor outcomes (19 studies), age (19 studies), behavior (six studies), sex (six studies), and affected side (six studies), with inconsistent evidence to support an association with changes in upper-extremity motor function after CIMT or bimanual training. Cognitive deficits, somatosensory deficits, and mixed effects of predictive factors were rarely studied. INTERPRETATION: Based on current evidence, it is inconclusive whether children with CP benefit from CIMT or bimanual training, regardless of their baseline upper-extremity motor function, age, sex, affected side, or behavior.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".