Gestational age at birth and morbidity, mortality, and growth in the first 4 years of life: findings from three birth cohorts in Southern Brazil
Bibliographic record
Abstract
BACKGROUND: We assessed anthropometric status, breastfeeding duration, morbidity, and mortality outcomes during the first four years of life according to gestational age, in three population-based birth cohorts in the city of Pelotas, Southern Brazil. METHODS: Total breastfeeding duration, neonatal mortality, infant morbidity and mortality, and anthropometric measures taken at 12 and 48 months were evaluated in children of different gestational ages born in 1982, 1993 and 2004 in Southern Brazil. RESULTS: Babies born <34 weeks of gestation and those born between 34-36 weeks presented increased morbidity and mortality, were breastfed for shorter periods, and were more likely to be undernourished at 12 months of life, in comparison with the 39-41 weeks group. Children born with 37 weeks were more than twice as likely to die in the first year of life, and were also at increased risk of hospitalization and underweight at 12 months of life. Post-term infants presented an increased risk of neonatal mortality. CONCLUSION: The increased risks of morbidity and mortality among preterm (<37 weeks of gestation) and post-term (>41 weeks) are well known. In our population babies born at 37 also present increased risk. As the proportion of preterm and early term babies has increased markedly in recent years, this is a cause for great concern.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".