Impact of Prenatal Risk Factors on Congenital Heart Disease in the Current Era
Bibliographic record
Abstract
BACKGROUND: The healthcare burden related to congenital heart disease (CHD) is increasing with improving survival. We assessed changing trends in prenatal risk factors for CHD in the current era in a Canadian cohort. METHODS AND RESULTS: CHD patients <18 years old (n=2339) and controls without structural heart disease (n=199) were prospectively enrolled in an Ontario province-wide biobank registry from 2008-2011. Family history, frequency of extra-cardiac anomalies (ECAs), and antenatal risk factors were assessed. Temporal trends were analyzed and associations with CHD were measured using linear and logistic regression. Family history of CHD and frequency of major ECAs was higher in cases versus controls (P<0.001). Despite an increase in genetic testing in the recent era, only 9.5% of cases with CHD had a confirmed genetic diagnosis. Yield of genetic testing (ie, frequency of abnormal results) was higher in familial and syndromic cases. There was an increase in parental age at conception, maternal prepregnancy body mass index, maternal urinary tract infections, type 1 diabetes, and exposure to nonfertility medications during pregnancy from 1990-2011. Later year of birth, family history of CHD, presence of major ECAs, maternal smoking during pregnancy, and maternal medication exposure were associated with increased odds of CHD (P<0.05 for all). Advanced parental age was associated with increased odds of CHD caused by genetic abnormalities. CONCLUSIONS: The increase in prenatal risk factors for CHD highlights the need for more rigorous ascertainment of genetic and environmental factors including gene-environment interactions that contribute to CHD.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".