Association of first trimester anaesthesia with risk of congenital heart defects in offspring
Bibliographic record
Abstract
BACKGROUND: A substantial number of pregnant women require anaesthesia for non-obstetric surgery, but the risk to fetal heart development is unknown. We assessed the relationship between first trimester anaesthesia and risk of congenital heart defects in offspring. METHODS: We conducted a longitudinal cohort study of 2 095 300 pregnancies resulting in live births in hospitals of Quebec, Canada, between 1990 and 2016. We identified women who received general or local/regional anaesthesia in the first trimester, including anaesthesia between 3 and 8 weeks post-conception, the critical weeks of fetal cardiogenesis. The main outcome measures were critical and non-critical heart defects in offspring. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association of first trimester anaesthesia with congenital heart defects, using log-binomial regression models adjusted for maternal characteristics. RESULTS: There were 107.3 congenital heart defects per 10 000 infants exposed to anaesthesia, compared with 87.2 per 10 000 unexposed infants. Anaesthesia between 3 and 8 weeks post-conception was associated with 1.50 times the risk of congenital heart defects (95% CI 1.11-2.03), compared with no anaesthesia. Anaesthesia between 5 and 6 weeks post-conception was associated with 1.84 times the risk (95% CI 1.10-3.08). Associations were driven mostly by general anaesthesia, which was associated with 2.49 times the risk between weeks 5 and 6 post-conception (95% CI 1.40-4.44). CONCLUSIONS: General anaesthesia during critical periods of fetal heart development may increase the risk of congenital heart defects. Further research is needed to confirm that anaesthetic agents are cardiac teratogens.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".