Association between prenatal antibiotic exposure and autism spectrum disorder among term births: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Prenatal antibiotic exposure induces changes in the maternal microbiome, which could influence the development of the infant's microbiome-gut-brain axis. OBJECTIVES: We assessed whether prenatal antibiotic exposure is associated with an increased risk of autism spectrum disorder (ASD) in offspring born at term. METHODS: This population-based retrospective cohort study included everyone who delivered a live singleton-term infant in British Columbia, Canada between April 2000 and December 2014. Exposure was defined as filling antibiotic prescriptions during pregnancy. The outcome was an ASD diagnosis from the British Columbia Autism Assessment Network, with a follow-up to December 2016. To examine the association among pregnant individuals treated for the same indication, we studied a sub-cohort diagnosed with urinary tract infections. Cox proportional hazards models were used to estimate unadjusted and adjusted hazard ratios (HR). The analysis was stratified by sex, trimester, cumulative duration of exposure, class of antibiotic, and mode of delivery. We ran a conditional logistic regression of discordant sibling pairs to control for unmeasured environmental and genetic confounding. RESULTS: Of the 569,953 children included in the cohort, 8729 were diagnosed with ASD (1.5%) and 169,922 were exposed to prenatal antibiotics (29.8%). Prenatal antibiotic exposure was associated with an increased risk of ASD (HR 1.10, 95% confidence interval [CI] 1.05, 1.15), particularly for exposure during the first and second trimesters (HR 1.11, 95% CI 1.04, 1.18 and HR 1.09, 95% CI 1.03, 1.16, respectively), and exposure lasting ≥15 days (HR 1.13, 95% CI 1.04, 1.23). No sex differences were observed. The association was attenuated in the sibling analysis (adjusted odds ratio 1.04, 95% CI 0.92, 1.17). CONCLUSIONS: Prenatal antibiotic exposure was associated with a small increase in the risk of ASD in offspring. Given the possibility of residual confounding, these results should not influence clinical decisions regarding antibiotic use during pregnancy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".