Traditional Versus Contemporary Models for Identifying Causes of Maternal Death: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: To examine differences in cause-of-maternal-death assignment based on underlying versus multiple causes of death. DESIGN: Cross-sectional, population-based study. SETTING: United States, 1999-2002 and 2018-2022. POPULATION: 1633 maternal deaths in 1999-2002 and 1929 maternal deaths in 2018-2022. METHODS: Causes of death were identified based on the underlying cause of death and also based on multiple causes of death. The frequency of six selected obstetrical causes of death was quantified and ranked. RESULTS: Pre-eclampsia (0.87 per 100 000 live births, 95% confidence interval [CI] 0.74-1.02) was the most common cause of maternal death in 2018-2022, when cause-of-death assignment was based on the underlying cause of death. Amniotic fluid embolism and cardiomyopathy tied for second rank, haemorrhage placed fourth and puerperal sepsis and uterine rupture tied for the fifth rank. Rankings based on multiple causes of death showed a different pattern: haemorrhage (1.13 per 100 000 live births, 95% CI 0.98-1.29) was the most common cause, followed by pre-eclampsia, cardiomyopathy, amniotic fluid embolism, puerperal sepsis and uterine rupture. There was no significant correlation between the cause-of-maternal death rankings based on the underlying and multiple causes of death (correlation coefficient 0.62, 95% CI-0.39, 0.95; p value 0.19) in 2018-2022. Cause-of-death rankings were significantly correlated under the two methods of cause-of-death assignment in 1999-2002 (correlation coefficient 0.83, 95% CI 0.05, 0.98; p value 0.04). CONCLUSION: Basing cause-of-death assignment and ranking of the causes of maternal death using a multiple causes-of-death approach may better inform clinical and public health priorities for reducing maternal mortality.
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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 | 0.052 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".