Influence of antibiotics given during labour and birth on body mass index <i>z</i> scores in children in the All Our Families pregnancy cohort
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Little is known about obesity risk associated with intrapartum antibiotic prophylaxis (IAP). Our objective was to determine if maternal antibiotic exposure during birth is associated with child body mass index (BMI) z scores in the first 3 years of life. METHODS: In 2008 to 2010, 3388 pregnant women were recruited to the All Our Families study. Here, we included women with available data from obstetrical records on antibiotic use during birth (n = 1303) and children with at least one valid BMI z score (final sample n = 1262). The primary outcome was infant BMI z score at 1, 2 and 3 years of age. RESULTS: IAP occurred in 432 of 1262 women. Children exposed to IAP had significantly higher mean [standard error (SE)] BMI z scores (1.071 [0.087] unit) at 1 year of age compared to non-exposed infants (0.744 [0.064] unit). Although the association was no longer significant after adjustment for confounding factors in the growth trajectory model, IAP resulted in a 0.255 unit increase in BMI z score at 1 year of age. Differences in BMI z score between exposed and non-exposed at baseline (year 1) only remained significant in sensitivity analysis. CONCLUSION: The potential association between maternal IAP and increased infant BMI z score at 1 year of age should be confirmed in other cohorts and warrants investigation of interventions to mitigate this possible risk.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".