Risk of congenital anomalies in pregnant users of non‐steroidal anti‐inflammatory drugs: a nested case‐control study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it