Associations of prenatal exposure to non‐steroidal anti‐inflammatory drugs with preterm birth and small for gestational age infants among women with autoimmune disorders
Bibliographic record
Abstract
PURPOSE: Estimate associations between prenatal non-steroidal anti-inflammatory (NSAID) exposure and preterm birth and small for gestational age among women with autoimmune conditions. METHODS: Participants were enrolled in the MotherToBaby cohort and had an autoimmune disorder and singleton live birth >20 weeks gestation (n = 2007). We characterized self-reported NSAID exposure over gestation for timing, duration, and average daily dose. Outcomes were preterm birth (i.e., <37 weeks' gestation) and small for gestational age infants (SGA; <10th percentile birthweight). We used Poisson regression to estimate associations between NSAID exposure and study outcomes adjusting for demographics, co-use of other medications (Model 1), and disease severity at baseline (Model 2). Secondarily, we considered the role of acetaminophen use by individually matching NSAID users to controls on cumulative dose of acetaminophen exposure. RESULTS: Overall, 15% of women reported NSAID use in pregnancy, with most use in the first trimester. No NSAID use exposure variables were associated with risk of preterm birth. Any NSAID use was associated with 1.7 (95% CI 1.2, 2.5) times greater risk of SGA and this estimate was attenuated to 1.5 (95% CI 1.0, 2.3) after adjustment for baseline disease severity. NSAID exposure in the first trimester was most strongly associated with SGA. After matching on acetaminophen exposure, associations between any NSAID use and preterm birth and SGA were 0.9 (95% CI 0.6, 1.4) and 1.8 (95% CI 1.1, 2.9). CONCLUSIONS: NSAID use in pregnancy is associated with SGA but not preterm birth. Future research should explore mechanisms that may explain these findings. Future research must also consider alternative explanations for these associations.
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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 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".