Perinatal antibiotic exposure of neonates in Canada and associated risk factors: a population-based study
Bibliographic record
Abstract
OBJECTIVE: To describe neonatal antibiotic exposures occurring immediately before and after birth and their associated risk factors. METHODS: A retrospective review of the hospital charts of 449 mother-neonate pairs enrolled in the Canadian Healthy Infant Longitudinal Development national birth-cohort study was conducted at two tertiary hospitals and one rural hospital in Manitoba, Canada. The main outcome measures included the following: maternal and neonatal antibiotic use during the perinatal period; indications for antibiotic use, including suspected neonatal sepsis, maternal group B Streptococcus (GBS), premature rupture of membranes and caesarean-section; maternal health status, focusing on gestational hypertension, gestational diabetes, obesity and primigravida pregnancies. RESULTS: During the perinatal period, 45.0% of neonates were exposed to antibiotics. Intravenous penicillin G (17%) and cefazolin (16%) were the most commonly administered intrapartum antibiotics. Colonization with GBS was confirmed in 21.2% of women and treated with antibiotics in 86% of cases. Overweight women and women with hypertension were significantly more likely to receive intrapartum antibiotics for caesarean section or GBS prophylaxis. Antibiotic treatment of the neonate was highest following emergency caesarean section (12%) or unknown maternal GBS status (20%). CONCLUSIONS: Neonates in Canada are routinely exposed to antibiotics during the perinatal period.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".