Autism Risk and Perinatal Antibiotic Use
Bibliographic record
Abstract
OBJECTIVES: Antibiotics are commonly administered during labor and delivery, and research has suggested that fetal exposure to antibiotics can increase risk for autism spectrum disorder (ASD). We assessed whether antibiotic exposure during labor and delivery increased the risk of ASD in the offspring. METHODS: This retrospective cohort study included everyone who delivered a live singleton-term infant in British Columbia, Canada, between April 1, 2000, and December 31, 2014. This cohort included 569 953 deliveries. To examine the association among pregnant individuals being treated for the same indication, we studied a subcohort of those who tested positive for group B Streptococcus. Cox proportional hazards models were used to estimate unadjusted and adjusted hazard ratios in both cohorts. A sensitivity analysis was conducted using length of first stage of labor as a proxy measure for dose to assess for a dose-response relationship. RESULTS: In this population-based study, antibiotic use during labor and delivery was not associated with an increased risk of ASD in offspring. The unadjusted and adjusted hazard ratios were 1.29 (95% confidence interval, 1.24-1.35) and 0.99 (0.94-1.04), respectively; and 1.07 (0.90-1.27) and 0.88 (0.74-1.05), respectively, in the group B Streptococcus-positive cohort. We observed no substantial difference in the association between antibiotic exposure and ASD depending on length of the first stage of labor. CONCLUSIONS: Our findings suggest that concern for ASD should not factor into the clinical decision on whether to administer antibiotics during labor and delivery. Future research is needed to examine longer durations of prenatal antibiotic exposure.
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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 | 0.000 | 0.005 |
| 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 | 0.001 | 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".