Modeling a gross motor curve of typically developing Dutch infants from 3.5 to 15.5 months based on the Alberta Infant Motor Scale
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Interindividual variability in gross motor development of infants is substantial and challenges the interpretation of motor assessments. Longitudinal research can provide insight into variability in individual gross motor trajectories. PURPOSE: To model a gross motor growth curve of healthy term-born infants from 3.5 to 15.5 months with the Alberta Infant Motor Scale (AIMS) and to explore groups of infants with different patterns of development. METHODS: A prospective longitudinal study including six assessments with the AIMS. A Linear Mixed Model analysis (LMM) was applied to model motor growth, controlled for covariates. Cluster analysis was used to explore groups with different pathways. Growth curves for the subgroups were modelled and differences in the covariates between the groups were described and tested. RESULTS: In total, data of 103 infants was included in the LMM which showed that a cubic function (F(1,571) = 89.68, p < 0.001) fitted the data best. None of the covariates remained in the model. Cluster analysis delineated three clinically relevant groups: 1) Early developers (32%), 2) Gradual developers (46%), and 3) Late bloomers (22%). Significant differences in covariates between the groups were found for birth order, maternal education and maternal employment. CONCLUSION: The current study contributes to knowledge about gross motor trajectories of healthy term born infants. Cluster analysis identified three groups with different gross motor trajectories. The motor growth curve provides a starting point for future research on motor trajectories of infants at risk and can contribute to accurate screening.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it