Motor trajectories of preterm and full‐term infants in the first year of life
Bibliographic record
Abstract
BACKGROUND: Motor development occurs throughout periods of motor skill acquisition, adjustment and variability. The objectives of this study were to analyze and compare biological and health characteristics and motor skill acquisition trajectories in preterm and full-term infants during the first year of life. METHODS: Two thousand, five hundred and seventy-nine infants (1,361 preterm) from 22 states were assessed using the Alberta Infant Motor Scale. Multivariate General Linear Model, t-tests, ANOVA, and Tukey tests were used. RESULTS: An age × group significant interaction was found for motor scores. On follow-up tests full-term infants had higher scores in prone, supine, sitting and standing postures that require trunk control from 9 to 10 months of age; although this advantage was observed for sitting from the second month of life. CONCLUSION: During the first trimester of life, preterm infants have higher scores in the supine and standing postures. Regarding motor trajectories, from newborn to 12 months, the period of higher motor acquisition was similar between full-term and preterm infants for prone (3-10 months), supine (1-6 months), and standing (6-12 months). For the sitting posture, however, full-term infants had a period of intensive motor learning of acquisition from the first to 7 months of life, whereas for preterm infants a shorter period was observed (3-7 months). CONCLUSION: Although the periods of higher motor acquisition were similar, full-term infants had higher scores in more control-demanding postures. Intervention for preterm infants needs to extend beyond the first months of life, and include guidance to parents to promote motor development strategies to achieve control in the higher postures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".