The risk of cardiovascular diseases after miscarriage, stillbirth, and induced abortion: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aims: Miscarriage and stillbirth have been included in cardiovascular disease (CVD) risk guidelines, however heterogeneity in exposures and outcomes and the absence of reviews assessing induced abortion, prevented comprehensive assessment. We aimed to perform a systematic review and meta-analysis of the risk of cardiovascular diseases for women with prior pregnancy loss (miscarriage, stillbirth, and induced abortion). Methods and results: Observational studies reporting risk of CVD, coronary heart disease (CHD), and stroke in women with pregnancy loss were selected after searching MEDLINE, Scopus, CINAHL, Web of Knowledge, and Cochrane Library (to January 2020). Data were extracted, and study quality were assessed using the Newcastle-Ottawa Scale. Pooled relative risk (RR) and 95% confidence intervals (CIs) were calculated using inverse variance weighted random-effects meta-analysis.Twenty-two studies involving 4 337 683 women were identified. Seven studies were good quality, seven were fair and eight were poor. Recurrent miscarriage was associated with a higher CHD risk (RR = 1.37, 95% CI: 1.12-1.66). One or more stillbirths was associated with a higher CVD (RR = 1.41, 95% CI: 1.09-1.82), CHD (RR = 1.51, 95% CI: 1.04-1.29), and stroke risk (RR = 1.33, 95% CI: 1.03-1.71). Recurrent stillbirth was associated with a higher CHD risk (RR = 1.28, 95% CI: 1.18-1.39). One or more abortions was associated with a higher CVD (RR = 1.04, 95% CI: 1.02-1.07), as was recurrent abortion (RR = 1.09, 95% CI: 1.05-1.13). Conclusion: Women with previous pregnancy loss are at a higher CVD, CHD, and stroke risk. Early identification and risk factor management is recommended. Further research is needed to understand CVD risk after abortion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it