Socioeconomic variations in the proportions of stroke attributable to reproductive profiles among postmenopausal women in China
Bibliographic record
Abstract
BACKGROUND: This prospective study aimed to examine the individual and combined population attributable fractions (PAFs) of stroke and its subtypes associated with reproductive factors among Chinese postmenopausal women, highlighting variations across socioeconomic status (SES) stratas. METHODS: Data were from 138,873 Chinese postmenopausal women enrolled in the China Kadoorie Biobank. Reproductive factors evaluated in this study included early age at menarche, early age at menopause, advanced age at first live birth, high parity, history of stillbirth, history of miscarriage or termination, and non-lactation. PAFs were calculated using hazard ratios, estimated using Cox proportional hazard regression, and prevalence of the seven reproductive factors. PAF for each reproductive risk factor and combined PAFs for all factors were estimated in total population and across SES classes. RESULTS: Of the 138,873 included participants, 17,042 developed strokes during a median follow-up period of 8.9 years. Across SES classes, the greatest attributable fractions of total stroke cases were observed for high parity among low-SES women (PAF 17.2%, 95% confidence interval [CI] 13.7%, 20.6%), history of miscarriage or termination among medium-SES women (PAF 11.4%, 95% CI 8.2%, 14.5%), and no history of lactation among high-SES women (PAF 3.1%, 95% CI 1.7%, 4.9%). A multiplicatively estimated 20.5% (95% CI 20.4%, 20.5%) and 3.1% (95% CI 1.7%, 4.9%) of stroke cases were attributable to the seven reproductive risk factors in low-SES and high-SES women, respectively. CONCLUSIONS: A large fraction of stroke cases among Chinese postmenopausal women were associated with reproductive factors. Targeted cardiovascular prevention strategies are warranted among women with different SES to mitigate risks associated with different reproductive profiles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".