Antihypertensive medication use during pregnancy and the risk of major congenital malformations or small‐for‐gestational‐age newborns
Bibliographic record
Abstract
BACKGROUND: In spite of the widespread use of antihypertensives during pregnancy, data on their risks and benefits for the newborn are limited. We investigated the risk of major congenital malformations or small-for-gestational-age newborns (SGA) in relation to gestational use of antihypertensives. METHODS: Within the Quebec Pregnancy Registry, we conducted two case-control studies. First, cases were defined as major congenital malformations diagnosed during the first year of life and controls were selected from the same cohort; index date was date of delivery. Gestational exposure was defined as filling a prescription for an antihypertensive during the 1st trimester of pregnancy. Next, cases (SGA) were defined as newborns with a birth weight <10th percentile for that gestational age and gender; controls were the newborns with a birth weight > or =10 percentile. Gestational exposure was defined as filling a prescription for an antihypertensive during the 2nd or 3rd trimester. Multivariate logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (95% CI). RESULTS: We found that overall antihypertensives use during the 2nd or 3rd trimesters of pregnancy was associated with a higher risk of SGA (OR 1.53, 95% CI 1.17-1.99). Moreover, selective beta-blocker (OR 6.00, 95% CI 1.06-33.87), alpha beta blocker (OR 2.26, 95% CI 1.04-4.88), or centrally-acting adrenergic agents use (OR 1.70, 95% CI 1.00-2.89) was associated with a higher risk of SGA compared to non-use. CONCLUSION: Gestational use of antihypertensives, especially beta-blocker, alpha beta blocker, or centrally-acting adrenergic agents, may increase the risk of SGA.
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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) | 0.001 | 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".