Motor development trajectories of children with cerebral palsy in a community-based early intervention program in rural South India
Bibliographic record
Abstract
Developmental trajectories are crucial for evidence-based prognostication, planning interventions, and monitoring progress in children with cerebral palsy (CP). To describe gross motor development patterns of children with CP in rural South India for the five Gross Motor Function Classification System (GMFCS) levels. Longitudinal cohort study of 302 children (176 males, 126 females) with CP aged 0 to 10 years, followed by a community-based early intervention program. GMFCS levels were 5.4 % level 1, 16.5 % level II, 22.8 % level III, 26.8 % level IV, and 28.5 % level V. Assessments were undertaken using the Gross Motor Function Measure (GMFM-66) at 6-month intervals between April 2017 and August 2020. Longitudinal analyses were performed using mixed-effects linear regression models. Five distinct motor development curves were created for ages 0 to 10 years by GMFCS levels as a function of age and GMFM-66 with a stable limit model, variation in estimated limits and rates of development. Motor development trajectories for CP in an LMIC differ from those reported in HICs. Consideration of how social determinants of health, environmental and personal factors impact motor development in low-resource contexts is crucial. Further work is needed to describe developmental trajectories of children for CP in LMICs. • Few longitudinal studies of children with cerebral palsy (CP) in LMICs exist. • Gross motor development in children with CP is influenced by contextual factors. • Developmental trajectories differ in an LMIC as compared to high-income countries. • Motor development in CP in LMICs shows a slower rate and lower limit than in HICs. • The Gross Motor Function Classification System for CP may need LMIC adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".