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.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".