Oseltamivir use in pregnancy: Risk of birth defects, preterm delivery, and small for gestational age infants
Bibliographic record
Abstract
BACKGROUND: Influenza infection during pregnancy increases risks for adverse outcomes for both mother and fetus. For this reason, treatment for infection or postexposure prophylaxis with a neuraminidase inhibitor, such as oseltamivir, may be needed. METHODS: Between 2009 and 2017, the Organization of Teratology Information Specialists (OTIS) MotherToBaby Pregnancy Studies enrolled pregnant women in the United States and Canada who were or were not treated with oseltamivir in a prospective cohort study. Data were collected on major birth defects, spontaneous abortion, preterm delivery, and small for gestational age birth size. Crude relative risks (RRs) or hazard ratios (HRs) were estimated together with 95% confidence intervals (CIs) for these outcomes. RESULTS: There were 716 subjects available for analysis; 112 were exposed to oseltamivir sometime in pregnancy and 604 were unexposed. 2/30 (6.7%) first-trimester-exposed pregnancies resulted in a fetus or infant with one or more major birth defects compared to 48/604 (7.9%) in the unexposed cohort (RR 0.84, 95% CI 0.19, 2.80). There were no spontaneous abortions reported. Risk of preterm delivery was not elevated in exposed versus comparison women (HR 0.65, 95% CI 0.26, 1.63). RRs for small for gestational age infants on weight, length, and head circumference following oseltamivir exposure anytime in pregnancy ranged from 0.70 to 1.30 with all 95% CIs including 1. CONCLUSION: We found no evidence of increased risks with oseltamivir for any of the outcomes evaluated. While numbers are small, these data are reassuring for pregnant women who need to be treated for infection or for postexposure prophylaxis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".