Intestinal microbial and metabolite profile in infants with small bowel stomas after bowel resection
Bibliographic record
Abstract
BACKGROUND: Infants with small bowel stomas (SBstoma) frequently struggle with absorption and rely on parenteral nutrition (PN). Intestinal absorption is difficult to predict based solely on intestinal anatomy. The purpose of this study was to characterize the microbiota and metabolic by-products within stoma effluent and correlate with clinical features and intestinal absorption. METHODS: Prospective cohort study collecting stoma samples from neonates with SBstoma (N = 23) or colostomy control (N = 6) at initial enteral feed (first sample) and before stoma closure (last sample). Gut bacteriome (16S ribosomal RNA [rRNA] sequencing), short-chain fatty acids (SCFAs) and bile acids (BAs) were characterized along with volume and energy content of a 48 h collection via bomb calorimetry (last sample). Hierarchical clustering and linear regression were used to compare the bacteriome and BAs/SCFAs, to bowel length, PN, and growth. RESULTS: Infants with ≤50% small bowel lost more fluid on average than those with >50% and controls (22, 18, 16 mL/kg/day, p = 0.013), but had similar energy losses (7, 10, 9 kcal/kg/day, p = 0.147). Infants growing poorly had enrichment of Proteobacteria compared to infants growing well (90% vs. 15%, p = 0.004). An increase in the ratio of secondary BAs within the small bowel over time, correlated with poor prognostic factors (≤50% small bowel, >50% of calories from PN, and poor growth). CONCLUSION: Infants with SBstoma and poor growth have a unique bacteriome community and those with poor enteral tolerance have metabolic differences compared to infants with improved absorption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".