Impact of postpartum hospital length of stay on infant gut microbiota: a comprehensive analysis of vaginal and caesarean birth
Bibliographic record
Abstract
BACKGROUND: The primary concern with prolonged hospitalization following birth is the risk of acquiring hospital-acquired infections (HAIs) caused by opportunistic bacteria, which can alter the early establishment of gut microbiota. OBJECTIVE: To assess the association between postpartum hospital length of stay (LOS) and the composition of gut microbiota at 3 and 12 months of age according to birth mode. METHODS: In total, 1313 Canadian infants from the CHILD Cohort Study were involved in this study. Prolonged LOS was defined as ≥2 days following vaginal delivery (VD) and ≥3 days following caesarean section (CS). The gut microbiota of infants was characterized by Illumina 16S rRNA sequencing of faecal samples at 3-4 months and 12 months of age. FINDINGS: Following prolonged LOS, VD infants with no exposure to intrapartum antibiotics had a higher abundance of bacteria known to cause HAIs in their gut, including Enterococcus spp. at 3 and 12 months, Citrobacter spp. at 3 months, and Clostridioides difficile at 12 months. Abundance of Enterococcus spp. or Citrobacter spp. at 3 months significantly mediated the association between LOS and low abundance of Bacteroidaceae, or higher Enterococcaeae/Bacteriodaceae or Enterobacterales/Bacteroidaceae abundance ratios at 12 months of age in VD infants without intrapartum antibiotic exposure. HAI-causing Enterobacterales were also more abundant in later infancy in infants with prolonged LOS following CS. In the absence of exclusive breastfeeding at 3 months or any breastfeeding at 12 months, Porphyromonadaceae (of Bacteroidota) were depleted in CS infants with prolonged LOS. CONCLUSIONS: Prolonged hospital stay after birth is associated with infant gut dysbiosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".