Bacterial Infections, Sepsis, and Multiorgan Failure in Cirrhosis
Bibliographic record
Abstract
Bacterial infections are an important complication of cirrhosis, particularly in hospitalized patients. In this article we review the prevalence, risk factors, and pathogenesis of bacterial infections in cirrhosis, focusing on the mechanisms of bacterial translocation such as impaired immunity and bacterial overgrowth, as well as maneuvers that may inhibit bacterial translocation and could be used not only to prevent infections but also to ameliorate the hyperdynamic circulatory state of cirrhosis. We also review the clinical features and management of the most common infection in cirrhosis, spontaneous bacterial peritonitis (SBP), specifically the evidence behind the therapy of acute SBP, the role of albumin, and the role of antibiotics in the prophylaxis of high-risk patients. It has been recognized that SBP and other bacterial infections lead to the systemic inflammatory response syndrome, sepsis, and multiorgan failure. We review the pathogenesis and management of these complications, the role of adrenal insufficiency, and the utility of intensive care prognostic models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".