Chapter 4 The impact of birth and postnatal medical interventions on infant gut microbiota
Bibliographic record
Abstract
Established during infancy, the initial colonisation and development of the complex gut microbial community of our gastrointestinal tract can be shaped by common medical interventions, such as caesarean section and antibiotic use. This chapter provides evidence on the gut microbial impact of four medical interventions: (1) caesarean delivery, (2) maternal intrapartum antibiotic prophylaxis (IAP), (3) hospitalisation post birth, and (4) postnatal infant antibiotic treatment. Reductions in bifidobacteria and members of the Bacteroidaceae family (e.g. Bacteroides fragilis) are by far the most common perturbations in microbial composition following each of these interventions, especially after elective or emergency caesarean section. On the other hand, genus Clostridium and the Enterbacteriaceae (e.g. Klebsiella, Escherichia coli) are likely to become more abundant in infants delivered by caesarean, exposed to maternal antibiotics, hospitalised post birth and treated with antibiotics. Often, the enterococci and staphylococci also become more abundant. Differential impact on gut microbiota is observed by type of caesarean delivery and antibiotic administered to the mother or infant. IAP with penicillin or cefazolin, or newborn treatment with intravenous penicillin (plus gentamicin) is associated with higher abundance of Enterococcus and Staphylococcus aureus. Klebsiella emerge after newborn intravenous ampicillin (plus gentamicin) treatment. The Veillonella become more abundant in the infant gut after emergency (but not elective caesarean), whereas they are found to be depleted two months after newborn treatment with oral cephalexin. Of note, dysbiosis from perinatal medical interventions also occurs in the early breastfed infant and is enhanced by prematurity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".