Evaluation of motor performance of preterm newborns during the first months of life using the Alberta Infant Motor Scale (AIMS)
Bibliographic record
Abstract
OBJECTIVES: To evaluate the motor performance of premature neonates using the Alberta Infant Motor Scale (AIMS) and to investigate the influence of birth weight on motor acquisition. METHODS: A cross-sectional study was carried out of a prospective cohort of 44 premature newborn infants with gestational ages from 32 to 34 weeks, without neurological disorders, selected from the neonatal intensive care unit at the Pontifícia Universidade Católica's Hospital São Lucas in Rio Grande do Sul, Brazil. The neonates studied were stratified by birth weight and assessed using the AIMS scale at the 40th week of postconceptional age, and at 4 and 8 months of corrected age. RESULTS: The preterm infants studied exhibited a progressive sequence of motor ability acquisition in all of the positions tested (prone, supine, sitting, standing), which occurred variable manner, expressed by the mean percentile of 43.2 to 45.7%, but within the limits of normality defined by the AIMS. It was observed that there was a clear increase in AIMS scores from the first to the last of the three postnatal observation points. The rate at which these scores increased was similar for both groups, irrespective of birth weight category (<1,750 g or >or= 1,750 g). CONCLUSIONS: The motor performance of the sample of premature infants studied here was normal according to the AIMS and their scores on that scale were not influenced by birth weight.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".