Maternal use of antibiotics and cancer incidence risk in offspring: A population‐based cohort study in Manitoba, Canada
Bibliographic record
Abstract
Several epidemiological studies have found an association between maternal antibiotics use during pregnancy and increased risk of certain cancer types, although conclusions differ between studies. We examined this association in a cohort study including 262 116 mother-child pairs of Manitoba births between 1996 and 2013. Maternal antibiotics use during prepregnancy (6 months prior to pregnancy) and pregnancy periods was assessed. Children's cancer incidence was tracked up to the end of the follow-up period (December 2015). We calculated incidence rate and used Cox regression to estimate adjusted hazard ratios (HRs). Antibiotics use during pregnancy was not associated with overall cancer (HR = 1.1, 95% confidence interval 0.9-1.4), leukemias (1.3, 0.9-1.8), or acute lymphocytic leukemia (1.1, 0.7-1.6). The association between antibiotics use and overall cancer risk differed by trimester: 1.5 (1.1-1.9) in the first, 0.8 (0.6-1.0) in the second, and 1.1 (0.8-1.5) in the third trimester. Further research is necessary to confirm the association between first-trimester exposure and cancer risk after a better controlling of confounding factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".