Impact of Postpartum Hospital Length-of-Stay on Infant Gut Microbiota: A Comprehensive Analysis of Vaginal and Caesarean birth
Bibliographic record
Abstract
Objectives This study aimed to assess the association between postpartum hospital length-of-stay and the composition of gut microbiota at 3 and 12 months of age in different birth modes. Design Prospective cohort of Canadian infants from the Canadian Healthy Infant Longitudinal Development (CHILD) Study born between 2008 and 2012. Setting General community. Sample 1313 infants from three study sites (Edmonton, Vancouver, and Winnipeg) of the CHILD cohort Methods Duration of hospital stay was documented in hospital records. Infants’ gut microbiota was characterized by Illumina 16S rRNA sequencing of fecal samples at 3 and 12 months. Main outcome measures Infant gut microbiota profiles. Results : In the absence of maternal intrapartum antibiotic (IAP) exposure, vaginally delivered infants (VD) with a longer hospital length-of-stay (LOS) had a higher abundance of bacteria in their gut known to cause hospital-acquired Infections (HAI), including Enterococcus at 3 months and 12 months and Citrobacter at 3 months of age. Moreover, HAI-causing bacteria Enterobacteriaceae were more abundant in later infancy in postnatal prolonged hospital stayed IAP-exposed caesarean section (CS) infants. Enterococcus or Citrobacter abundance at 3 months significantly mediated the association of LOS with low relative abundance of Bacteroidaceae and a high relative abundance of Enterococcaeae/Bacteriodaceae or Enterobacteriaceae/Bacteroidaceae ratio at 12 months of age in VD infants without IAP exposure. Conclusions LOS after birth is associated with infant gut dysbiosis. Further research is needed to explore the health outcomes of these associations.
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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".