Long-Term Mortality in Women With Pregnancy Loss and Modification by Race/Ethnicity
Bibliographic record
Abstract
Pregnancy loss is a common reproductive complication, but its association with long-term mortality and whether this varies by maternal race/ethnicity is not well understood. Data from a racially diverse cohort of pregnant women enrolled in the Collaborative Perinatal Project (CPP) from 1959 to 1966 were used for this study. CPP records were linked to the National Death Index and the Social Security Death Master File to identify deaths and underlying cause (until 2016). Pregnancy loss comprised self-reported losses, including abortions, stillbirths, and ectopic pregnancies. Among 48,188 women (46.0% White, 45.8% Black, 8.2% other race/ethnicity), 25.6% reported at least 1 pregnancy loss and 39% died. Pregnancy loss was associated with a higher absolute risk of all-cause mortality (risk difference, 4.0 per 100 women, 95% confidence interval: 1.4, 6.5) and cardiovascular mortality (risk difference, 2.2 per 100 women, 95% confidence interval: 0.8, 3.5). Stratified by race/ethnicity, a higher risk of mortality persisted in White, but not Black, women. Women with recurrent losses are at increased risk of death, both overall and across all race/ethnicity groups. Pregnancy loss is associated with death; however, it does not confer an excess risk above the observed baseline risk in Black women. These findings support the need to assess reproductive history as part of routine screening in 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".