Pre-pregnancy body mass index and other risk factors for early- and late-onset hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome: A population-based retrospective cohort study.
Bibliographic record
Abstract
Background : Obesity increases risk of pre-eclampsia, but the association with hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome is understudied. Objective: To examine the association between pre-pregnancy body-mass-index (BMI) and HELLP syndrome, including early- vs. late-onset disease. Study Design: A retrospective cohort study, population-based data. Setting: British Columbia (BC), Canada, 2008/09-2019/20. Population: All pregnancies resulting in live births or stillbirths at ≥20 weeks’ gestation. Methods: BMI categories (kg/m 2 ) included: underweight (<18.5), normal (18.5-24.9), overweight (25.0-29.9), and obese (≥30.0). Rates of early- and late-onset HELLP syndrome (<34 vs. ≥34 weeks, respectively) were calculated per 1000 ongoing pregnancies at 20- and 34-weeks’ gestation, respectively. Cox regression was used to assess the associations between risk factors (BMI and, e.g., maternal age, parity) and early- vs late-onset HELLP syndrome. Main outcome measures: HELLP syndrome. Results: The rates of HELLP syndrome per 1000 women were 2.8 overall (1,116 per 391,941 women), and 1.9, 2.5, 3.2 and 4.0 in underweight, normal BMI, overweight and obese categories, respectively. Overall, gestational age-specific rates increased with pre-pregnancy BMI. Adjusted hazard ratio [AHR] was 2.24 for early-onset (95% confidence interval [CI] 1.65-3.04) vs. AHR 1.48 (95% CI 1.23-1.80) for late-onset HELLP syndrome (p-value for interaction 0.025). Chronic hypertension, multiple gestation, hemorrhage (<20 weeks’ gestation and antepartum) also showed differing AHRs between early- vs. late-onset HELLP. Conclusions: Pre-pregnancy BMI is positively associated with HELLP syndrome and the association is stronger with early-onset HELLP syndrome. Associations with early- and late-onset HELLP syndrome differed for some risk factors, suggesting possible differences in etiologic mechanisms.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".