New-Onset Hypertension in Late Pregnancy is Associated with Lower Fetal and Infant Mortality in Preterm Twins
Bibliographic record
Abstract
OBJECTIVE: To assess the association between new-onset hypertension in late pregnancy (NOH) and fetal and infant mortality in early preterm, late preterm, and full-term twins. METHODS: We conducted a retrospective cohort study in 275, 316 twins in 1995-1997 based on multiple birth registration dataset of USA. Generalized estimating equations (GEEs) was used to evaluate the odds ratios (OR) of fetal and infant death (at individual level) associated with NOH, with adjustment of potential confounders at both twin set level and individual level. RESULTS: The risks for early neonatal death (OR = 0.52, 95% CI: 0.36, 0.76) and late neonatal death (OR = 0.57, 95% CI: 0.37, 0.87) were decreased in early preterm twins born to mothers with NOH compared with early preterm twins born to mothers with normal blood pressure. The decreased risks for fetal death (OR = 0.40, 95% CI: 0.30, 0.53; OR = 0.46, 95% CI: 0.53, 0.65) and infant death (OR = 0.35, 95% CI: 0.28, 0.44; OR = 0.68, 95% CI: 0.51, 0.91) were associated with NOH in both early and late preterm twins, whereas no association between NOH and fetal/infant mortality were observed in full-term twins. CONCLUSION: NOH is associated with lower risk of fetal death and infant death in preterm twins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".