Weighted Cumulative Exposure Modelling to Assess the Association Between Reproductive Factors and Future Cardiovascular Disease in Women
Bibliographic record
Abstract
BACKGROUND: The occurrence of reproductive or pregnancy events, such as severe maternal morbidity (SMM), may reveal a predisposition to chronic disease and premature mortality. However, most studies have examined these exposures without considering their timing, severity, or recurrence. OBJECTIVES: We propose using a weighted cumulative exposure (WCE) modelling approach to flexibly describe the relationship between reproductive events and longer-term health outcomes in a longitudinal cohort of pregnant women. METHODS: Application of the WCE modelling approach is accomplished in three steps. First, relative weights are estimated from a multivariable Cox proportional hazards model corresponding to the association of each reproductive risk factor with a given health outcome. Then, a longitudinal dataset is constructed in which all reproductive predictors are recorded at regular intervals (every 3 months), beginning 42 days after each woman's first birth in the cohort and ending at an outcome or censoring event. A new multivariable Cox model applied to this longitudinal dataset, incorporating time-varying WCE-derived reproductive risk scores along with simple time-varying reproductive and non-reproductive predictors, is estimated. Finally, adjusted WCE-based hazard ratios (HR) associated with different reproductive event exposure histories are calculated. RESULTS: In the cohort of 1,992,972 births in Canada (excluding Quebec), 2008-2021, with mean (SD) follow-up time in the longitudinal dataset of 7.3 ± 3.8 years, we propose to use the WCE approach to predict outcomes such as premature cardiovascular disease (16,846 cardiovascular hospitalisations observed, or 1.19 per 1000 person-years). CONCLUSIONS: Use of flexible WCE modelling to quantify risks of pregnancy events such as SMM, adjusted for reproductive and non-reproductive CVD risk factors, will account for variation in timing and severity of these events and will capture their cumulative effects across a woman's reproductive trajectory. This approach can refine estimates of etiologic associations and inform novel clinical prediction models with the potential to predict postpartum long-term health outcomes for a given woman based on her unique reproductive history.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".