Neurodevelopmental Treatment in Children With Cerebral Palsy: A Review of the Literature
Bibliographic record
Abstract
This review aimed to explore the current literature on neurodevelopmental treatment (NDT) in children with cerebral palsy (CP). It also sought to determine what outcome measures are used to analyze the effect of NDT and whether these parameters are in line with the components of the International Classification of Functioning, Disability and Health (ICF). The studies published in the English language between 2000 and 2023 were included based on a search of the databases PEDro, PubMed, and Google Scholar. Studies that examined the effect of NDT on children with CP were included. We found a total of 54 studies describing the effect of NDT in children with CP and these were included in this literature review. NDT in children with CP was found to have positive outcomes in 41 studies, while 13 studies had contradictory conclusions. Based on our findings, NDT is widely used for the rehabilitation of children with CP globally. The parameters used to assess the improvement mostly included gross motor function, balance, and postural control. The outcome measures used in studies are usually linked to body structure and function or activities domain of the ICF model by the World Health Organization (WHO). However, there is a scarcity of studies on the effect of NDT on participation, which should be the outcome of any rehabilitation program. There is scope for future research to demonstrate the effect of NDT on the participation of children with CP. Further studies with larger sample sizes and homogenous groups are recommended.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".