Impact of intrapartum antibiotics on the infant gastrointestinal microbiome: a narrative review
Bibliographic record
Abstract
Background The composition of the infant gastrointestinal (GI) microbiome has been linked to adverse long-term health outcomes and neonatal sepsis. Several factors are known to impact the composition of the microbiome, including mode of delivery, gestational age, feeding method and exposure to antibiotics. The impact of intrapartum antibiotics (IPAs) on the infant microbiome requires further research. Objective We aimed to evaluate the impact of IPAs on the infant GI microbiome. Methods We searched Ovid MEDLINE and Embase Classic+Embase for articles in English reporting on the microbiome of infants exposed to IPAs from the date of inception to 3 January 2021. Primary outcomes included abundance and colonisation ofBifidobacteriumandLactobacillus, as well as alpha and beta diversity. Results 30 papers were included in this review. In the first year of life, following exposure to IPAs, 30% (6/20) of infant cohorts displayed significantly reducedBifidobacterium, 89% (17/19) did not display any significant differences inLactobacilluscolonisation, 21% (7/34) displayed significantly reduced alpha diversity and 35% (12/34) displayed alterations in beta diversity. Results were further stratified by delivery, gestational age (preterm or full term) and feeding method. Conclusions IPAs impact the composition of the infant GI microbiome, resulting in possible reductionsBifidobacteriumand alpha diversity, and possible alterations in beta diversity. Our findings may have implications for maternal and neonatal health, including interventions to prevent reductions in health-promoting bacteria (eg, probiotics) and IPA class selection.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".