Birth Weight Independently Affects Morbidity and Mortality of Extremely Preterm Neonates
Bibliographic record
Abstract
BACKGROUND: Neonates born between 24 + 0 and 27 + 6 gestational weeks, widely known as extremely preterm neonates, present a category characterized by increased neonatal mortality and morbidity. Main objective of the present study is to analyze the effect of various epidemiological and pregnancy-related parameters on unfavorable neonatal mortality and morbidity outcomes. METHODS: A retrospective study was performed enrolling cases delivered during 2003 - 2008 in our department. Cases of neonatal death as well as pathological Apgar score (≤ 4 in the first and ≤ 7 in the fifth minute of life), need for emergency resuscitation, respiratory disease syndrome (RDS), neonatal asphyxia, intraventricular hemorrhage (IVH) and neonatal death were recorded for neonates of our analysis. A multivariate regression model was used to correlate these outcomes with gestational week at delivery, maternal age, parity, kind of gestation (singleton or multiple), intrauterine growth restriction (IUGR), birth weight (BW), preterm premature rupture of membranes (PPROM), mode of delivery (vaginal delivery or cesarean section) and antenatal use of corticosteroids. RESULTS: Out of 5,070 pregnancies delivered, 57 extremely preterm neonates were born (1.1%). Mean BW was 780.35 ± 176.0, RDS was observed in 93.0% (n = 53), resuscitation was needed in 54.4% (n = 31) while overall mortality rate was 52.6% (n = 30). BW was independently associated with neonatal death (P = 0.004), pathological Apgar score in the first (P = 0.05) and fifth minute of life (P = 0.04) as well as neonatal sepsis (P = 0.05). CONCLUSION: BW at delivery is independently affecting neonatal mortality and morbidity parameters in extremely preterm neonates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".