Risk of congenital anomalies in pregnant users of non‐steroidal anti‐inflammatory drugs: a nested case‐control study
Bibliographic record
Abstract
BACKGROUND: Many women take non-steroidal anti-inflammatory drugs (NSAIDs) during pregnancy but the risks for the infant remain controversial. We carried out a study to quantify the association between those women prescribed NSAIDs in early pregnancy and congenital anomalies. METHODS: A population-based pregnancy registry was built by linking data from three administrative databases in Quebec between 1997-2003. The inclusion criteria were mothers of live singleton infants, between 15-45 years of age, covered by the RAMQ drug plan > or =12 months before and during pregnancy, and prescribed an NSAID or other medications during pregnancy. We selected as cases infants with any congenital anomaly (ICD-9; 740-759) diagnosed in the first year of life. Up to 10 controls, defined as infants with no congenital anomalies detected were selected for each case. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were estimated. RESULTS: Within the registry, 36,387 pregnant women met the inclusion criteria. We identified 93 births with congenital anomalies in 1056 women (8.8%) who filled prescriptions for NSAIDs in the first trimester of pregnancy, compared to 2478 in 35,331 (7%) women who did not. The adjusted OR for any congenital anomalies for women who filled a prescription for NSAIDs in the first trimester was 2.21 (95% CI = 1.72-2.85). The adjusted OR for the anomalies related to cardiac septal closure was 3.34 (95% CI = 1.87-5.98). There were no significant associations with anomalies of other major organ systems. CONCLUSIONS: Our study suggests that women prescribed NSAIDs during early pregnancy may be at a greater risk of having children with congenital anomalies, specifically cardiac septal defects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".