Telehealth Program for Infants at Risk of Cerebral Palsy during the Covid-19 Pandemic: A Pre-post Feasibility Experimental Study
Bibliographic record
Abstract
Aim: To verify the effects of a telerehabilitation program for infants at high risk for Cerebral Palsy (CP) during the COVID-19 pandemic. Method: Longitudinal study. Infants were aged 3–18 months corrected age, at risk of developmental delay. The General Movement Assessment or a neurologic examination were performed to identify the risk of CP. Motor function was assessed using the Gross Motor Function Measure-88 (GMFM-88) and the Alberta Infant Motor Scale (AIMS). Caregivers of infants at high risk of CP applied a home-based program supervised by a Physical therapist, five times a week over 12 weeks. The program included guidance for optimal positioning, optimization of goal-directed activities, environmental enrichment, and educational strategies. Results: 100 infants at risk for delayed motor development were recruited. Eighteen infants were classified at high risk of CP, and 10 families completed telerehabilitation (83% final retention rate). No adverse events were reported. Adherence to the telecare program was high (90%). The costs were low. We found increased scores for all dimensions and the total score of the GMFM-88, and the AIMS percentile at the end of the intervention. Most infants presented a clinically significant change for the GMFM-88. Conclusions: The telecare program was feasible.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.001 |
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".