Pregnancy‐induced hypertension and infant mortality: roles of birthweight centiles and gestational age
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of pregnancy-induced hypertension (PIH) on infant mortality in different birthweight centiles (small for gestational age [SGA], appropriate for gestational age [AGA], and large for gestational age [LGA]) and gestational ages (early preterm, late preterm, and full term). DESIGN: Retrospective cohort study. SETTING: Linked birth and infant death data set of USA between 1995 and 2000. POPULATION: A total of 17 464 560 eligible liveborn singleton births delivered after 20th gestational week. METHODS: Multivariate logistic regression models were applied to evaluate the association between PIH and infant mortality, with adjustment of potential confounders stratified by birthweight centiles and gestational age. MAIN OUTCOME MEASURE: Infant death (0-364 days) and its three components: early neonatal death (0-6 days), late neonatal death (7-27 days), and postneonatal death (28-364 days). RESULTS: PIH was associated with decreased risks of infant mortality, early neonatal mortality, and late neonatal mortality in both preterm and term SGA births, and PIH was associated with lower postneonatal mortality in preterm SGA births. PIH was associated with decreased risks of infant mortality, early neonatal mortality, late neonatal mortality and postneonatal mortality in preterm AGA births. Decreased risk of infant mortality and early neonatal mortality was associated with PIH in early preterm LGA births. CONCLUSIONS: The association between PIH and infant mortality varies depending on different birthweight centiles, gestational age, and age at death. PIH is associated with a decreased risk of infant mortality in SGA births, preterm AGA births, and early preterm LGA births.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 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".