Hospitalized Infants With Medical Complexity Experience Slow Acquisition of Gross Motor Skills
Bibliographic record
Abstract
OBJECTIVES: The progression of infant gross motor development during an acute hospitalization is unknown. Understanding gross motor skill acquisition in hospitalized infants with complex medical conditions is necessary to develop and evaluate interventions that may lessen delays. Establishing a baseline of gross motor abilities and skill development for these infants will guide future research. The primary purposes of this observational study were to: (1) describe gross motor skills of infants with complex medical conditions (n = 143) during an acute hospitalization and (2) evaluate the rate of change in gross motor skill development in a heterogenous group of hospitalized infants with prolonged length of stay (n = 45). METHODS: Gross motor skills in hospitalized infants aged birth to 18 months receiving physical therapy were evaluated monthly using the Alberta Infant Motor Scale. Regression analysis was completed to assess rate of change in gross motor skills. RESULTS: Of the 143 participants, 91 (64%) demonstrated significant motor delay at initial evaluation. Infants with prolonged hospitalization (mean 26.9 ± 17.5 weeks) gained new gross motor skills at a significant rate of 1.4 points per month in Alberta Infant Motor Scale raw scores; however, most (76%) continued with gross motor delays. CONCLUSIONS: Infants with complex medical conditions admitted for prolonged hospitalization frequently have delayed gross motor development at baseline and have slower than typical acquisition of gross motor skills during hospitalization, gaining 1.4 new skills per month compared with peers acquiring 5 to 8 new skills monthly. Further research is needed to determine effectiveness of interventions designed to mitigate gross motor delay in hospitalized infants.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".