Impact of Late Preterm Birth on Neonatal Intensive Care Resources in a Tertiary Perinatal Center
Bibliographic record
Abstract
OBJECTIVE: To examine delivery indications, short-term morbidities, and use of resources for late preterm infants admitted to the neonatal intensive care unit (NICU) at a tertiary perinatal center. STUDY DESIGN: Data for 1137 inborn infants 340/7 to 366/7 weeks' gestational age discharged between July 2004 and December 2009 were collected from an electronic NICU database. Birth information was obtained from maternal charts. RESULTS: Forty-two percent of late preterm infants were admitted to the NICU. Their mean ( ± standard deviation) birth weight was 2347 ± 569 g; 15.1% were small for gestational age, 35.5% were multiples, and 17.8% had an antenatally diagnosed anomaly. Most births (52%) occurred following spontaneous rupture of membranes or labor. Cesarean section rate was 56.8%. Mortality rate was 1.2%. Most frequent morbidities were transient tachypnea (18.8%), cardiac or other congenital anomaly (16.8%), and respiratory distress syndrome (7.4%). Although 41.5% received ventilatory support, duration was short (1.1 ± 3.1 days). Mean length of NICU stay was 8.1 ± 9.3 days with 38% transferred to community hospitals before discharge. CONCLUSION: For many late preterm infants admitted to the NICU, the duration of intensive therapy was short and some required no interventions. One impact of late preterm birth was bed occupancy.
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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 | 0.001 | 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) | 0.003 | 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".