Predicting Age of Independent Walking in Preterm Infants: A Longitudinal Study Using Neonatal Characteristics and Motor Development Variables
Bibliographic record
Abstract
OBJECTIVE: To formulate an equation estimating months to independent walking in moderate to late preterm infants based on neonatal characteristics and gross motor development from 7 months to independent walking. METHODS: Sixty infants born between 32 to 36 weeks were assessed using Alberta Infant Motor Scale (AIMS) for gross motor development. Neonatal characteristics were recorded at 7 months, and caregiver-reported independent walking onset. Pearson correlation analyzed age, AIMS scores, and neonatal factors. Multiple regression developed the prediction equation. RESULTS: The equation for independent walking onset, which included gestational age (GA) at birth, total AIMS score at 10 months of age (10th AIMS), and birth head circumference (BHC), exhibited a strong correlation (r=0.707) and had a predictive power of 50.0%. The equation is as follows: age onset of independent walking (months)=33.157, -0.296 (GA), -0.132 (10th AIMS), -0.196 (BHC), with an estimation error of 0.631 months. CONCLUSION: Neonatal characteristics, such as GA, 10th AIMS, and BHC, are key determinants in estimating the onset of independent walking in moderate to late preterm 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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".