An association between antibiotic usage during pregnancy and the subsequent development of autoimmune hepatitis
Bibliographic record
Abstract
Background: Whether the incidence of autoimmune hepatitis (AIH) is higher in women prescribed antibiotics during pregnancy remains to be determined. Methods: Administrative databases and hospital abstracts were reviewed to identify all pregnant women in the province of Manitoba from 1996 to 2001 who were prescribed antibiotics during pregnancy and subsequently diagnosed with AIH until 2020. Results: In this study, 70,666 pregnant women were identified during the 5-year period. Antibiotics were prescribed in 11,654 (16.5%). AIH was subsequently diagnosed in more mothers who were prescribed antibiotics than in those who were not (82/11,654 [0.7%] versus 166/58,975 [0.28%], p <0.0001). The number of antibiotic prescriptions was higher in mothers who subsequently developed AIH than in those who were prescribed antibiotics but did not develop AIH (0.56 [SD 1.09] versus 0.24 [SD 0.67], p <0.0001) as were the prescribed durations of treatment (4.19 [SD 7.72] versus 2.03 [SD 7.55] days, p <0.0001). The mean ages (25.9 [SD 6.0] and 26.8 [SD 5.8]; years) and times to AIH diagnosis (10.6 [SD 4.9] and 10.8 [SD 4.8] years) were similar in mothers who were prescribed and not prescribed antibiotics and developed AIH. The relative risk of developing AIH in antibiotic recipients was 2.5 (95% CI 1.92–3.25, p <0.0001), and the hazard ratio was 2.58 (95% CI 1.98–3.36, p <0.001). Conclusions: These results describe an association between mothers prescribed antibiotics during pregnancy and the subsequent development of AIH.
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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 | 0.008 |
| 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) | 0.001 | 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".