Non-cardiac birth defects and long-term risk of cardiovascular hospitalisation
Bibliographic record
Abstract
BACKGROUND: Patients with heart defects are at risk of developing cardiovascular disease. Our objective was to determine if non-cardiac birth defects are associated with the risk of cardiovascular hospitalisation. METHODS: We conducted a longitudinal cohort study of 1 451 409 parous women in Quebec, Canada. We compared patients with cardiac and non-cardiac birth defects of the urinary, central nervous and other systems against patients without defects between 1989 and 2022. The main outcome was hospitalisation for coronary artery disease, ischaemic stroke and other cardiovascular outcomes during 33 years of follow-up. We computed cardiovascular hospitalisation rates and used Cox proportional hazards regression models to measure the association (HR; 95% CI) between non-cardiac defects and later risk of cardiovascular hospitalisation, adjusted for patient characteristics. RESULTS: Women with any birth defect had a higher rate of cardiovascular hospitalisation than women without defects (7.0 vs 3.3 per 1000 person-years). Non-cardiac defects overall were associated with 1.61 times the risk of cardiovascular hospitalisation over time, compared with no defect (95% CI 1.56 to 1.66). Isolated urinary (HR 3.93, 95% CI 3.65 to 4.23), central nervous system (HR 3.33, 95% CI 2.94 to 3.76) and digestive defects (HR 2.39, 95% CI 2.16 to 2.65) were associated with the greatest risk of cardiovascular hospitalisation. These anomalies were associated with cardiovascular hospitalisation whether they presented alone or clustered with other defects. Nevertheless, heart defects were associated with the greatest risk of cardiovascular hospitalisation (HR 10.30, 95% CI 9.86 to 10.75). CONCLUSION: The findings suggest that both cardiac and non-cardiac birth defects are associated with an increased risk of developing cardiovascular disease among parous 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".