Is Gestational Hypertension Protective against Perinatal Mortality in Twin Pregnancies?
Bibliographic record
Abstract
BACKGROUND: Pregnancy-induced or gestational hypertension is a common pregnancy complication. Paradoxically, gestational hypertension has been associated with a protective effect against perinatal mortality in twin pregnancies in analytic models (logistic regression) without accounting for survival time. Whether this effect is real remains uncertain. This study aimed to validate the impact of gestational hypertension on perinatal mortality in twin pregnancies using a survival analysis approach. METHODS: This was a retrospective cohort study of 278,821 twin pregnancies, using the U.S. 1995-2000 matched multiple birth dataset (the largest dataset available for multiple births). Cox proportional hazard models were applied to estimate the adjusted hazard ratios (aHR) of perinatal death (stillbirth and neonatal death) comparing gestational hypertensive vs. non-hypertensive pregnancies controlling for maternal characteristics and twin cluster-level dependence. RESULTS: Comparing births in gestational hypertensive vs. non-hypertensive twin pregnancies, perinatal mortality rates were significantly lower (1.20% vs. 3.38%), so were neonatal mortality (0.72% vs. 2.30%) and stillbirth (0.48% vs. 1.10%) rates. The aHRs (95% confidence intervals) were 0.34 (0.31-0.38) for perinatal death, 0.31 (0.27-0.34) for neonatal death, and 0.45 (0.38-0.53) for stillbirth, respectively. The protective effect of gestational hypertension against perinatal death became weaker over advancing gestational age; the aHRs in very preterm (<32 weeks), mild preterm (32-36 weeks) and term (37+ weeks) births were 0.29, 0.48 and 0.76, respectively. The largest risk reductions in neonatal mortality were observed for infections and immaturity-related conditions. CONCLUSIONS: Gestational hypertension appears to be beneficial for fetal survival in twin pregnancies, especially in those ending more prematurely or for deaths due to infections and immaturity-related conditions. Prospective studies are required to rule out the possibility of unmeasured confounders.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".