Late preterm birth has direct and indirect effects on infant gut microbiota development during the first six months of life
Bibliographic record
Abstract
AIM: Preterm infants display aberrant gut microbial colonisation. We investigated whether the differences in gut microbiota between late preterm and full-term infants results from prematurity or external exposures. METHODS: ) and 75 full-term infants based on faecal samples collected following birth and at two to four weeks and six months of age. We assessed clinically relevant bacteria using quantitative polymerase chain reaction. Logistic regression analyses were performed to determine whether the observed differences in gut microbiota were attributable to prematurity or perinatal exposure. RESULTS: The prevalence of bifidobacteria differed in the intestinal microbiota of the full-term and late preterm neonates. Differences in the presence of specific species were detected at the age of six months, although the microbiota alterations were most prominent following delivery. As well as prematurity, the mode of birth, intrapartum and neonatal antibiotic exposure, and the duration of breastfeeding had an additional impact on gut microbiota development. CONCLUSION: The gut microbiota composition was significantly different between late preterm and full-term infants at least six months after birth. Antibiotic exposure was common in late preterm infants and modulated gut colonisation, but preterm birth also affected gut microbiota development independently.
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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 | 0.000 |
| 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".