Intrapartum antibiotics for GBS prophylaxis alter colonization patterns in the early infant gut microbiome of low risk infants
Bibliographic record
Abstract
Early life microbial colonization and succession is critically important to healthy development with impacts on metabolic and immunologic processes throughout life. A longitudinal prospective cohort was recruited from midwifery practices to include infants born at full term gestation to women with uncomplicated pregnancies. Here we compare bacterial community succession in infants born vaginally, with no exposure to antibiotics (n = 53), with infants who were exposed to intrapartum antibiotic prophylaxis (IAP) for Group B Streptococcus (GBS; n = 14), and infants born by C-section (n = 7). Molecular profiles of the 16 S rRNA genes indicate that there is a delay in the expansion of Bifidobacterium, which was the dominate infant gut colonizer, over the first 12 weeks and a persistence of Escherichia when IAP for GBS exposure is present during vaginal labour. Longer duration of IAP exposure increased the magnitude of the effect on Bifidobacterium populations, suggesting a longer delay in microbial community maturation. As with prior studies, we found altered gut colonisation following C-section that included a notable lack of Bacteroidetes. This study found that exposure of infants to IAP for GBS during vaginal birth affected aspects of gut microbial ecology that, although dramatic at early time points, disappeared by 12 weeks of age in most infants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".