Meta-Analysis of Risk Factors for Congenital Heart Disease: Part 2, Maternal Medication, Reproductive Technologies, and Familial and Fetal Factors
Bibliographic record
Abstract
BACKGROUND: The quantitative effects of congenital heart disease (CHD) risk factors are not fully understood. We conducted a meta-analysis of all CHD risk factors. This report explores maternal medication, assisted reproductive technologies (ART), and familial and fetal factors. METHODS: Relevant studies were identified using a search strategy encompassing the concepts of CHD and prenatal risk factors with the following inclusion criteria: (1) peer-reviewed articles, (2) quantifying the effects of CHD risk factors, and (3) between 1989 and 2022. Pooled odds ratios (OR) and 95% confidence intervals (CIs) were calculated using a random effect model. RESULTS: There were 131 articles that met the inclusion criteria. Associations were found between CHDs and extracardiac anomalies (OR, 3.41; 95% CI, 1.72-6.77), increased nuchal translucency (OR, 6.87; 95% CI, 2.42-19.53), family history of CHD (OR, 2.90; 95% CI, 2.25-3.75), maternal antidepressants (OR, 1.23; 95% CI, 1.09-1.38), and antihypertensives (OR, 2.07; 95% CI, 1.80-2.38). A positive association was observed between severe CHDs and lithium, but with a very wide CI encompassing the null effect. A positive association was observed between severe CHDs and ARTs (OR, 1.98; 95% CI, 1.30-3.02). The data were insufficient for anomalies of the umbilical cord, anticonvulsants, and retinoid medication. CONCLUSIONS: There were strong associations among CHDs and increased nuchal translucency, extracardiac anomalies, and family history of CHD. Effect sizes were modest for maternal medication and ART. Data were scarce and sometimes inconclusive for some risk factors commonly cited as being associated with CHD such as lithium, anomalies of the umbilical cord, anticonvulsants, and retinoid medication.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".