Relationship between early neurological function and motor skills in Brazilian preterm infants: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Preterm infants have an increased risk of neurodevelopmental delays, with consequences that may persist in later life. Thus, monitoring their development is needed for early detection and targeted intervention. This study aimed to assess the correlation between neurological function and motor skills in preterm infants from 2 to 6 months of corrected age after hospital discharge in an outpatient follow-up program. METHODS: This cross-sectional study involved 50 preterm infants with a mean gestational age of 32.0 (± 2.8) weeks and a mean birth weight of 1645.2 (± 576.5) grams. Neurological function and motor development were assessed once between two to six months of corrected age by using the Hammersmith Infant Neurological Examination (HINE) and Alberta Infant Motor Scale (AIMS), respectively. Data on pregnancy, type of delivery, and birth were obtained using interviews with family and medical records. The Spearman correlation coefficient assessed the association between HINE and AIMS scores. RESULTS: A significant correlation was found between HINE categories and AIMS subscales: cranial nerve function versus prone (r = 0.55; p < 0.001); posture versus prone (r = 0.69; p < 0.001); posture versus sitting (r = 0.67; p < 0.001); posture versus standing (r = 0.63; p < 0.001); tone versus sitting (r = 0.51; p < 0.001); tone versus standing (r = 0.49; p < 0.001); and total HINE versus total AIMS (r = 0.77; p < 0.001). CONCLUSIONS: The present study found strong correlation between neurological function and motor skills in preterm infants during the first months of life. Whilst the HINE and AIMS can complete each other, where there are limited resources the HINE may be appropriate to administer as a single assessment.
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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 | 0.001 |
| 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".