Risk factors associated with severe preeclampsia and HELLP syndrome in singleton and twin pregnancies: a population-based cohort study
Bibliographic record
Abstract
Objective: To compare pre-pregnancy risk factors associated with severe preeclampsia/eclampsia (SPE) and/or HELLP syndrome among singleton versus twin pregnancies. Design: A population-based retrospective cohort study. Setting: British Columbia (BC), Canada. Population: All pregnancies with singletons or twins that resulted in a stillbirth or live birth at ≥20 weeks’ gestation from 2008/09 to 2020/21. Methods: Data were obtained from the BC Perinatal Database Registry. Logistic regression was used to estimate the association between each risk factor (e.g., body-mass-index (BMI), in-vitro-fertilization (IVF), chronic hypertension, and diabetes) and SPE/HELLP, as well as the modifying effect of plurality. Main Outcome Measures: Severe preeclampsia, eclampsia, and/or HELLP syndrome. Results: Among 563,252 pregnancies (8,841 twin, 554,411 singleton), the rate of SPE/HELLP was 4.7 per 1,000 singleton pregnancies and 31.1 per 1,000 twin pregnancies (relative risk 6.61, 95% confidence interval [CI] 5.84-7.49). Older maternal age (≥35 years), nulliparity, pre-pregnancy and gestational diabetes, chronic hypertension, prior mental health problems, substance use during pregnancy and prior stillbirth increased the odds, while smoking decreased the odds of SPE/HELLP among both singletons and twins. However, the adjusted associations between BMI, IVF, prior abortions and SPE/HELLP differed by plurality: IVF and high BMI were associated with elevated risks in singleton pregnancies but not in twins, while a history of prior abortions was associated with decreased risk in twin but not singleton pregnancies. Conclusions: High BMI and IVF are associated with elevated risk of SPE/HELLP syndrome in singleton pregnancies, but not in twin pregnancies. This study provides insights regarding SPE/HELLP syndrome among singleton and twin pregnancies, and useful information for pre-pregnancy counselling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".