Association of maternal chronic disease with risk of congenital heart disease in offspring
Bibliographic record
Abstract
BACKGROUND: Information about known risk factors for congenital heart disease is scarce. In this population-based study, we aimed to investigate the relation between maternal chronic disease and congenital heart disease in offspring. METHODS: The study cohort consisted of 1 387 650 live births from 2004 to 2010. We identified chronic disease in mothers and mild and severe forms of congenital heart disease in their offspring from Taiwan's National Health Insurance medical claims. We used multivariable logistic regression analysis to assess the associations of all cases and specific types of congenital heart disease with various maternal chronic diseases. RESULTS: For mothers with the following chronic diseases, the overall prevalence of congenital heart disease in their children was significantly higher than for mothers without these diseases: diabetes mellitus type 1 (adjusted odds ratio [OR] 2.32, 95% confidence interval [CI] 1.66-3.25), diabetes mellitus type 2 (adjusted OR 2.85, 95% CI 2.60-3.12), hypertension (adjusted OR 1.87, 95% CI 1.69-2.07), congenital heart defects (adjusted OR 3.05, 95% CI 2.45-3.80), anemia (adjusted OR 1.31, 95% CI 1.25-1.38), connective tissue disorders (adjusted OR 1.39, 95% CI 1.19-1.62), epilepsy (adjusted OR 1.37, 95% CI 1.08-1.74) and mood disorders (adjusted OR 1.25, 95% CI 1.11-1.41). The same pattern held for mild forms of congenital heart disease. A higher prevalence of severe congenital heart disease was seen only among offspring of mothers with congenital heart defects or type 2 diabetes. INTERPRETATION: The children of women with several kinds of chronic disease appear to be at risk for congenital heart disease. Preconception counselling and optimum treatment of pregnant women with chronic disease would seem prudent.
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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 | 0.003 |
| 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) | 0.002 | 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".