Association Between Maternal Chronic Conditions and Congenital Heart Defects
Bibliographic record
Abstract
BACKGROUND: This study quantifies the association between maternal medical conditions/illnesses and congenital heart defects (CHDs) among infants. METHODS AND RESULTS: We carried out a population-based study of all mother-infant pairs (n=2,278,838) in Canada (excluding Quebec) from 2002 to 2010 using data from the Canadian Institute for Health Information. CHDs among infants were classified phenotypically through a hierarchical grouping of International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Canada codes. Maternal conditions such as multifetal pregnancy, diabetes mellitus, hypertension, and congenital heart disease were defined by use of diagnosis codes. The association between maternal conditions and CHDs and its subtypes was modeled using logistic regression with adjustment for maternal age, parity, residence, and other factors. There were 26 488 infants diagnosed with CHDs at birth or at rehospitalization in infancy; the overall CHD prevalence was 116.2 per 10,000 live births, of which the severe CHD rate was 22.3 per 10,000. Risk factors for CHD included maternal age ≥40 years (adjusted odds ratio [aOR], 1.48; 95% confidence interval [CI], 1.39-1.58), multifetal pregnancy (aOR, 4.53; 95% CI, 4.28-4.80), diabetes mellitus (type 1: aOR, 4.65; 95% CI, 4.13-5.24; type 2: aOR, 4.12; 95% CI, 3.69-4.60), hypertension (aOR, 1.81; 95% CI, 1.61-2.03), thyroid disorders (aOR, 1.45; 95% CI, 1.26-1.67), congenital heart disease (aOR, 9.92; 95% CI, 8.36-11.8), systemic connective tissue disorders (aOR, 3.01; 95% CI, 2.23-4.06), and epilepsy and mood disorders (aOR, 1.41; 95% CI, 1.16-1.72). Specific CHD subtypes were associated with different maternal risk factors. CONCLUSIONS: Several chronic maternal medical conditions, including diabetes mellitus, hypertension, connective tissue disorders, and congenital heart disease, confer an increased risk of CHD in the offspring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".