Pregnancy Loss and Risk of All-Cause Mortality in Chinese Women: Findings From the China Kadoorie Biobank
Bibliographic record
Abstract
Objectives: Pregnancy loss is a common obstetric complication that may be associated with maternal mortality. However, evidence is sparse and inconsistent. This study aims to investigate the association between pregnancy loss with the risk of all-cause mortality among Chinese women. Methods: Data on 299,582 women aged 30–79 years old from the China Kadoorie Biobank were used. Cox proportional hazard regression was conducted to investigate the association between the occurrence of pregnancy loss and all-cause mortality. Results: Two or more pregnancy losses was associated with long-term all-cause mortality (adjusted hazard ratio (aHR) of 1.10, 95% CI: 1.03–1.18). Specifically, more than one spontaneous abortion or stillbirth was associated with long-term all-cause mortality (aHR 1.10, 95% CI: 1.01–1.21 and 1.14, 95% CI: 1.04–1.25, respectively). When stratified by the presence of cardiovascular disease or diabetes, as well as age at baseline, two or more pregnancy losses in women aged ≥50 diagnosed with cardiovascular disease (aHR 1.32, 95% CI: 1.18–1.48) or diabetes (aHR 1.30, 95% CI: 1.06–1.60) was associated with all-cause mortality. Conclusion: Recurrent pregnancy loss, in particular two or more spontaneous abortions and stillbirths were associated with increased risk of all-cause mortality. The associations between recurrent pregnancy losses and all-cause mortality were more pronounced in women aged ≥50 with cardiovascular disease or diabetes at baseline.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".