Gross Motor Performance Among Late Preterm Infants
Bibliographic record
Abstract
Background: Late preterm infants are the neonates whose birth occur on the beginning of 239th day (34 0/7 weeks’ gestation) to the ending of 259th day (36 6/7 weeks’ gestation) since the onset of the first day of mother’s last normal menstrual period presented with delayed milestones in terms of their gross motor performance. Objective: To describe gross motor performance among late preterm infants in relation to their chronological age. Methodology: An observational descriptive cross-sectional survey on a total of 49 patients (n=49) was conducted at Children Hospital, Lahore with non-probability convenient sampling technique (1). Late preterm infants, both male and female between age 2-18 months were included, diagnosed cases of cerebral palsy, Spina bifida, Developmental dysplasia of hip, Down syndrome and Muscular dystrophy were excluded. Alberta infant motor scale (AIMS) was used for assessment after taking written consent from parents of infants (2). Results: The mean age of 49 infants were 8.15 months and the standard deviation 4.479. There were 27 (55%) males and 22 (45%) females out of 49 infants. Out of Forty-Nine late preterm infants, 31(63%) infants were presented with atypical performance, 7(14%) with suspected performance, 7(14%) with normal performance, 1(2%) with good performance and 3(6%) with excellent performance as indicated by their respective percentile ranks. Conclusion: Late preterm infants show lower gross motor performance as most of the infants were presented with atypical performance. Keywords: Late preterm infants, Gross motor performance, Alberta infant motor scale
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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 | 0.000 | 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".