Impact of Late Preterm and Early Term Infants on Canadian Neonatal Intensive Care Units
Bibliographic record
Abstract
OBJECTIVE: To examine the short-term morbidities, mortality, and use of neonatal intensive care unit (NICU) resources for late preterm, early term, and term infants. STUDY DESIGN: Infants born between 34 and 40 weeks of gestation and admitted to a Canadian NICU in 2010 were designated late preterm (340/7 to 366/7 weeks), early term (370/7 to 386/7 weeks), or term (390/7 to 406/7 weeks). Mortality, short-term morbidities, and resource utilization were compared between the three groups using chi-square tests and analysis of variance. RESULTS: Among 6,636 included infants, 44.2% (n = 2,935) were late preterm, 26.2% (n = 1,737) early term, and 29.6% (n = 1,964) term. Term infants were more likely to require resuscitation at birth and had lower Apgar scores than late preterm and early term infants (p < 0.001). Length of stay and need for respiratory support decreased with increasing gestational age; however, the proportion of hospital days that intensive care was required increased. CONCLUSION: The greatest impact of late preterm infants is on NICU bed occupancy, whereas for term infants it is on intensity of care. Early term infants experience greater rates of some complications than term, demonstrating that risk persists for these infants. These findings have important implications for NICU resource planning and practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".