Impact of hypertensive disorders of pregnancy on neonatal outcomes among infants born at 24+0–31+6 weeks’ gestation in China: A multicenter cohort study
Bibliographic record
Abstract
Objective To describe the rate of hypertensive disorder of pregnancy (HDP) among mothers of very preterm infants (VPIs) admitted to Chinese neonatal intensive care units (NICUs), and to investigate the relationship between HDP and the outcomes of VPIs. Study design Cohort study of all VPIs born at a gestational age of 24+0–31+6weeks and admitted to 57 tertiary NICUs of the Chinese Neonatal Network (CHNN) in 2019. Infants with severe congenital anomalies or missing maternal HDP information were excluded. Two multivariate logistic regression models were generated to assess the relationship between HDP and neonatal outcomes. Results Among 9,262 infants enrolled, 1,744 (18.8%) infants were born to mothers with HDP, with an increasing incidence with increasing gestational age. VPIs born to mothers with HDP had higher gestational age but lower birth weight and were more likely to be small for gestational age. Mothers with HDP were more likely to receive antenatal steroids, MgSO4and cesarean section. Infants in the HDP group showed higher observed rates of mortality or any morbidity than infants in the non-HDP group (50.2% vs. 47.2%, crude odds ratio (OR) 1.13, 95% CI 1.02–1.26). However, the associations between HDP and adverse outcomes were not significant after adjustment. In the HDP group, mothers of 1,324/1,688 (78.4%) infants were diagnosed with preeclampsia/eclampsia. Infants born to mothers with preeclampsia/eclampsia had significantly lower odds of early death and severe retinopathy of prematurity. Conclusions Nearly one-fifth of VPIs were born to mothers with HDP in Chinese NICUs. No significant association was identified between HDP and adverse neonatal short-term outcomes of VPIs, while long-term follow-up of these infants is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".