Gut Microbiome Function and Recovery after Critical Illness
Bibliographic record
Abstract
Over five million patients require intensive care annually, and up to three-quarters of survivors develop new disability one year later. This constellation of physical, psychological, and cognitive impairments—termed post-intensive care syndrome (PICS)—remains without effective therapy. Emerging evidence implicates persistent inflammation, insulin resistance, and catabolism as key drivers of long-term impairment. Gut microbiota play a central role in regulating these same pathways. Through the production of fecal microbiota-derived metabolites, the gut microbiota influence inflammation, insulin sensitivity, intestinal barrier integrity, and cross-talk with the brain and skeletal muscle. Critically ill patients routinely develop disruption of the gut microbiome, or dysbiosis, characterized by loss of diversity and altered fecal metabolite profiles. In prior work, I discovered that loss of gut microbiota function—termed metabolic dysbiosis—was associated with progression of respiratory failure and intensive care unit (ICU) mortality, suggesting that impaired gut microbiome function may contribute to adverse outcomes in critically ill patients. The overall objective of this proposal is to determine whether fecal metabolic dysbiosis is associated with long-term physical and cognitive impairment in ICU survivors. In Aim 1, we will identify fecal metabolite profiles associated with longitudinal one-year recovery trajectories of cognitive function using the Montreal Cognitive Assessment (MoCA). In Aim 2, we will define fecal metabolite profiles linked to long-term physical impairment using Short Form-36 (SF-36) quality-of-life measures. Both aims will leverage existing multi-omic data from a well-characterized cohort of ICU survivors and apply machine learning methods to identify metabolite profiles associated with post-ICU recovery trajectories.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".