Trends in eclampsia in the United States, 2009–2017: a population-based study
Bibliographic record
Abstract
Background: Reducing the prevalence of eclampsia, a major cause of maternal and perinatal morbidity, is a maternal health priority. However, sparse data exist examining trends in the USA prevalence of eclampsia. Objective: The aim of this study was to assess temporal trends in the prevalence of eclampsia among live births in the United States from 2009 to 2017. Study design: This population-based cross-sectional study included live births in 41 USA states and the District of Columbia between 2009 and 2017. The prevalence of eclampsia among all women, women with chronic hypertension and hypertensive disorders of pregnancy were reported by 1000 live births. Risk ratios adjusted for maternal characteristics were used to assess temporal trends. Results: Of 27 866 714 live births between 2009 and 2017, 83 000 (0.30%) were associated with eclampsia. The adjusted risk of eclampsia decreased 10% during the 7 most recent years of the cohort, with an adjusted risk ratio of 0.90 [95% confidence interval (95% CI): 0.87–0.93] in 2017 relative to 2009. Relative to 2009, the adjusted risk of eclampsia in 2017 was substantially lower among women with chronic hypertension (adjusted risk ratio: 0.51; 95% CI: 0.46–0.57) and women with hypertensive pregnancy disorders (adjusted risk ratio: 0.43; 95% CI: 0.40–0.47). Among nonhypertensive women, there was a slight increase in the adjusted risk of eclampsia in 2017 relative to 2009 (adjusted risk ratio: 1.14; 95% CI: 1.10–1.17). Conclusion: Despite reductions in the eclampsia prevalence among women with chronic hypertension and hypertensive disorders of pregnancy, public health initiatives are needed to reduce the overall eclampsia prevalence, especially in nonhypertensive women.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".