Effectiveness of motor interventions in infants with cerebral palsy: a systematic review
Bibliographic record
Abstract
AIM: To systematically review the evidence on the effectiveness of motor interventions for infants from birth to 2 years with a diagnosis of cerebral palsy or at high risk of it. METHOD: Relevant literature was identified by searching journal article databases (PubMed, Embase, CINAHL, Cochrane, Web of Knowledge, and PEDro). Selection criteria included infants between the ages of birth and 2 years diagnosed with, or at risk of, cerebral palsy who received early motor intervention. RESULTS: Thirty-four studies met the inclusion criteria, including 10 randomized controlled trials. Studies varied in quality, interventions, and participant inclusion criteria. Neurodevelopmental therapy was the most common intervention investigated either as the experimental or control assignment. The two interventions that had a moderate to large effect on motor outcomes (Cohen's effect size>0.7) had the common themes of child-initiated movement, environment modification/enrichment, and task-specific training. INTERPRETATION: The published evidence for early motor intervention is limited by the lack of high-quality trials. There is some promising evidence that early intervention incorporating child-initiated movement (based on motor-learning principles and task specificity), parental education, and environment modification have a positive effect on motor development. Further research is crucial.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".