Maternal Hypertension and Neonatal Outcome Among Small for Gestational Age Infants
Bibliographic record
Abstract
OBJECTIVE: To determine whether maternal hypertension might improve perinatal outcome among small for gestational age (SGA) infants (< 10th percentile). METHODS: Our prospective cohort comprised 17 Canadian neonatal intensive care units (NICUs) and 3,244 SGA singletons. Multivariable regression was used to analyze the relation between maternal hypertension and each of the following: SNAP-II (Score of Neonatal Acute Physiology; ordinal regression) and neonatal survival and survival without severe intraventricular hemorrhage (logistic regression), adjusting for potential confounders. RESULTS: There were 698 (21.5%) neonates born to hypertensive mothers. Inversely associated with lower SNAP-II scores (healthier infant) were antenatal steroids (complete course: odds ratio [OR] 0.67, 95% confidence interval [CI] 0.54-0.83; incomplete: OR 0.71, 95% CI 0.56-0.88), lower gestational age (< 27 weeks: OR 0.06, 95% CI 0.05-0.08; 27-28 weeks: OR 0.11, 95% CI 0.07-0.17; 29-32 weeks: OR 0.28, 95% CI 0.23-0.35), 5-minute Apgar < 7 (OR 0.30, 95% CI 0.25-0.36), male gender (OR 0.80, 95% CI 0.70-0.92), and anomalies (OR 0.49, 95% CI 0.41-0.58). Maternal hypertension was associated with lower SNAP-II (healthier infant) (7.54 +/- 11.16 [hypertensive] versus 7.21 +/- 11.85 [normotensive]) on multivariable regression analysis (adjusted OR 1.25, 95% CI 1.05-1.49), as well as higher neonatal survival (93.0% versus 91.2%, and adjusted OR 1.9, 95% CI 1.2-3.0), but not survival without severe intraventricular hemorrhage (91.4% versus 87.0%, and adjusted OR 1.4, 95% CI 1.0-2.0), respectively. CONCLUSION: Among SGA neonates in NICU, maternal hypertension is associated with improved admission neonatal physiology and survival.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".