INFLUÊNCIA DO BAIXO PESO AO NASCER SOBRE O DESEMPENHO MOTOR DE LACTENTES A TERMO NO PRIMEIRO SEMESTRE DE VIDA
Bibliographic record
Abstract
This study aimed to verify the influence of low birth weight (LBW) on gross motor performance in the first semester of life of term infants born small for their gestational age (SGA), comparing them to a controlled group born with an appropriate weight for their gestational age (AGA). To verify the influence of LBW on motor development, infants classified as premature and with diseases diagnosed at birth were excluded from the sample. A cross-sectional and double-blinded study for infants at the 3 rd and 6 th month of life was performed. The motor performance was measured applying the Alberta Infant Motor Scale, which classifies infants using a development curve in which the scores vary between 5% and 90%. The F-test was used to compare the groups for variances and the t-test was used for averages (significant level fixed at 5%). In the 3 rd month of life, the SGA group presented a lower average percentile than the AGA group; however such difference was not statistically significant. At the 6 th month of life, the average percentile of the AGA group was higher than the SGA group, with significant difference (p < 0.05). Both infant groups presented instability in the acquisition of gross motor skills and average percentile, lower than the Canadian normative group (50%). It was concluded that the birth weight might have influenced the motor performance of the group, favoring the AGA group. The results will contribute to the precocious diagnosis of motor alterations, as well as to the knowledge of the risk factors for the infantile development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".