Early reduction in gut microbiota diversity in critically ill patients is associated with mortality
Bibliographic record
Abstract
BACKGROUND: Critical illness is associated with an altered gut microbiota, yet its association with poor outcomes remains unclear. This study evaluates the early gut microbiota diversity changes in intensive care unit patients and its association with mortality. Additionally, it explores fecal pH as a potential biomarker for these changes. METHODS: In this prospective observational cohort study, fecal samples were collected at two time points: S1, the first stool passed upon intensive care unit admission, and S2, the first stool passed at least 24 h after S1. Full-length 16S rRNA gene sequencing was performed for gut microbiota analysis, with α-diversity measured using the Shannon index. Bayesian joint models were used to estimate the association between time-varying changes in gut microbiota diversity and 60-day mortality, as well as the association between daily changes in stool pH and in diversity. RESULTS: Twenty-four of 96 patients overall died during follow-up. Daily Shannon index decreased on average by -0.1 points [95% Credible Intervals (CrI) -0.20 to -0.10]. Every point decrease in Shannon index was associated with a 1.99-fold increase in the hazard of death (95% CrI, 1.04 to 4.51). Time-varying fecal pH levels were not associated with changes in Shannon index. CONCLUSIONS: Gut microbiota diversity decreased over time, associated with increased mortality. Fecal pH is an unreliable marker of gut microbiota change. Future studies on gut microbiota and related biomarkers should focus on the initial days in the intensive care unit to detect and mitigate a decline in gut microbiota diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".