Antibiotic prophylaxis in cirrhosis: Good and bad
Bibliographic record
Abstract
UNLABELLED: Patients with cirrhosis, particularly those with decompensated cirrhosis, are at increased risk of bacterial infections that may further precipitate other liver decompensations including acute-on-chronic liver failure. Infections constitute the main cause of death in patients with advanced cirrhosis, and strategies to prevent them are essential. The main current strategy is the use of prophylactic antibiotics targeted at specific subpopulations at high risk of infection: prior episode of spontaneous bacterial peritonitis, upper gastrointestinal bleeding, and low-protein ascites with associated poor liver function. Antibiotic prophylaxis effectively prevents not only the development of bacterial infections in all these indications but also further decompensation (variceal bleeding, hepatorenal syndrome) and improves survival. However, antibiotic prophylaxis is also associated with a clinically relevant and increasing drawback, the development of infections due to multidrug-resistant organisms. Several strategies have been suggested to balance the risks and benefits of antibiotic prophylaxis. CONCLUSION: Antibiotic stewardship principles such as the restriction of antibiotic prophylaxis to subpopulations at a very high risk for infection, the avoidance of antibiotic overuse, and early deescalation policies are key to achieve this balance; nonantibiotic prophylactic measures such as probiotics, prokinetics, bile acids, statins, and hematopoietic growth factors could also contribute to ameliorate the development and spread of multidrug-resistant bacteria in cirrhosis. (Hepatology 2016;63:2019-2031).
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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".