PP-39 Prenatal antibiotic exposure and childhood asthma: a population-based study
Bibliographic record
Abstract
Importance Antibiotic use during infancy alters gut microbiota and immune development, and is associated with an increased risk of childhood asthma. The impact of prenatal antibiotic exposure is unclear. Objective To determine and characterise the associa-tion of prenatal antibiotic exposure and childhood asth-ma. Design Population-based cohort study using admin-istrative healthcare data. Antibiotic use was determined from prescription records. Asthma was defined using hospitalisation records, physician billing claims, and pre-scription records. Associations were determined using Cox regression and expressed as hazard ratios (HR) and 95% confidence intervals (CI). Setting General population in Manitoba, Canada. Participants 2 13 661 mother-child dyads born from 1996–2012. Exposure Maternal antibiotic use. Outcome Child asthma, defined as meeting any of the following criteria after 5 years of age: any hospitalisation for asthma; or ≥2 physician diagnoses of asthma, at least 3 months apart and within a 1 year period; or ≥2 prescrip-tions for asthma medications within a 1 year period. Results In our study population, 10.1% of children met the case definition for asthma, and 36.8% were prena-tally exposed to antibiotics. Prenatal antibiotic exposure was associated with an increased risk of asthma (crude HR 1.29; 95% CI 1.26–1.33). This association persisted af-ter controlling for maternal asthma, sex, location of resi-dence, gestational age, number of siblings, and postna-tal antibiotic exposure during infancy (adjusted HR 1.23; 1.20–1.27). However, maternal antibiotic use during the 9 months before pregnancy (adjusted HR 1.28, 1.24–1.31) and 9 months postpartum (adjusted HR 1.32, 1.29–1.36) were similarly associated with childhood asthma. Conclusions and Relevance Maternal antibiotic use before, during and after pregnancy was associated with a modest, dose-dependent increase in asthma risk among offspring. While our study does not support a pregnan-cy-specific causal relationship between maternal antibi-otic use and childhood asthma, it remains important to prescribe and use antibiotics judiciously.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".