Impact of Bacterial Translocation on Sarcopenia in Patients with Decompensated Cirrhosis
Bibliographic record
Abstract
Advanced liver disease is associated with a persistent inflammatory state, derived from abnormal bacterial translocation from the gut, which may contribute to the development of sarcopenia in cirrhosis. We aim to document the association of chronic inflammation and bacterial translocation with the presence of sarcopenia in cirrhosis. We prospectively followed cirrhotic patients aged 18–70 years with medically refractory ascites at a single tertiary care center in Toronto, Canada. The baseline data included patient demographic variables, the presence of bacterial DNA in serum/ascitic fluid, systemic inflammatory response syndrome (SIRS) status, and nutritional assessment. Thirty-one patients were enrolled, 18 (58.1%) were sarcopenic, 9 (29%) had bacterial DNA in serum and ascites fluid. The mean MELD score was 11.5 ± 4.0 (6–23). Sarcopenic and non-sarcopenic patients did not differ significantly in their baseline MELD scores, caloric intake, resting energy expenditure, the incidence of bacterial translocation, or SIRS. While sarcopenia was not linked to increased hospital admissions or death, it was strongly associated with increased episodes of acute kidney injury (3 vs. 0, p = 0.05). This pilot study did not demonstrate an association between sarcopenia and SIRS or bacterial translocation. These results should be confirmed in future larger studies, encompassing a greater number of chronic inflammation events and quantifying levels of bacterial DNA.
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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) | 0.000 | 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".