Motor behavior of low birth weight and very low birth weight premature infants
Bibliographic record
Abstract
ABSTRACT The objectives of this study were to verify the difference between the motor behaviors of low birth weight (LBW) premature infants and very low birth weight (VLBW) premature infants in the first 8 months of life and assess motor behavior of these infants in different age groups. We evaluated 41 LBW infants (2499g to 1500g) and 22 VLBW infants (1499g to 1000g). Groups were divided according to age into NB-1 month, 2-4 months, and 5-8 months, and their motor behavior was analyzed by the Alberta Infant Motor Scale (AIMS) score and percentile. Kruskal-Wallis test was utilized to verify the difference of motor behavior between age groups (NB to 1 month, 2-4 months, and 5-8 months) in each group independently. To detect differences between groups (LBW and VLBW) in each age group, we used the Mann-Whitney test (p≤0.05). A significant difference was observed between the LBW group and the VLBW group, both for score (p=0.011) and percentiles (p=0.010), in age groups 2-4 months and 5-8 months (p=0.017; p=0.013, respectively). In the comparison between age groups 0-1 month and 2-4 months, we observed higher scores for the LBW (p=0.000) and VLBW (p=0.001) groups and lower percentiles (p=0.003) for the VLBW group at 2-4 months. Between age groups 0-1 month and 5-8 months, we observed higher scores (p=0.000; p=0.000) and lower percentiles (p=0.005; p=0.000) at 5-8 months. Between age groups 2-4 months and 5-8 months, we observed higher score (p=0.000; p=0.000) and lower percentile (p=0.006; p=0.004) at 5-8 months. Birth weight had a significant impact on motor development of premature infants, and developmental delays can be more evident at older ages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".