Bacterial infections in end-stage liver disease: current challenges and future directions
Bibliographic record
Abstract
Bacterial infections continue to be a leading cause of mortality andacute-on-chronic liver failure in end-stage liver disease (ESLD). The consequences of infection include prolonged hospitalisation, acute kidney injury (AKI), death, de-listing from liver transplant and susceptibility to further infections. The diagnosis of infections in cirrhosis is fraught due to the background of a partial systemic inflammatory response syndrome (SIRS) state and negative cultures in 30-50% of patients. Furthermore, the lack of multi-center studies limits the generalisability of currently available results. The modulation of infections by the underlying immune state, gut barrier function and super-imposed medications such as beta-blockers, proton pump inhibitors and antibiotics is required. A rational approach to the diagnosis and prevention of AKI associated with infection, withjudicious use of crystalloids and albumin, is also needed. Changes in bacteriology including emergence of multi-resistant organisms and Clostridium difficile have also recently changed the approach for prophylaxis and therapy of infections. Effective strategies for the prevention, diagnosis, and management of infections in ESLD form a large unmet need. A systematic approach to study the epidemiology, bacteriology, resistance patterns, and procedure and medication utilisation specific to ESLD is needed to improve outcomes. Bacterial infections in patients with end-stage liver disease affect candidacy for liver transplantation. Up to one-third of all hospitalised patients with cirrhosis are infected.1–5 With sepsis, mortality increases to more than 50% and is associated with significant costs.6 A recent systematic review demonstrated a fourfold increased risk of death in infected cirrhotic patients compared with their non-infected counterparts.7 More importantly, intensive care unit (ICU) mortality of patients with cirrhosis has remained unchanged over 50 years, unlike disease states such as cardiac failure where mortality has decreased.8 Therefore the prevention, diagnosis …
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".