Prophylactic antibiotics in patients with alcohol‐associated hepatitis receiving steroids: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background & Aims The benefits of prophylactic antibiotics in patients with alcohol‐associated hepatitis (AH) receiving steroids remain unclear. We aimed to assess the clinical impact of prophylactic antibiotics in AH patients receiving steroids. Methods We systematically reviewed four electronic databases from inception to 30 November 2023. Pooled estimates were analysed using random‐effects models. The primary outcome was 90‐day survival. Secondary outcomes included infection at days 30 and 90 days, hepatorenal syndrome (HRS), acute kidney injury (AKI), hepatic encephalopathy (HE) and drug‐related adverse events (AE). Trial sequential analyses were performed for the primary outcome of 90‐day mortality. Results We screened 419 articles and included six eligible studies (four RCTs and two matched cohort studies) with a total of 510 patients. Compared to standard medical treatment (SMT), prophylactic antibiotics were associated with a lower risk of infection at 30 days (OR: 0.35, 95%CI: 0.20–0.59, I 2 = 0%), infection at 90 days (OR: 0.26, 95%CI: 0.10–0.67, I 2 = 0%) and a lower rate of HE (OR: 0.32, 95%CI: 0.12–0.87, I 2 = 0%). However, prophylactic antibiotics did not improve 90‐day survival, sepsis‐related mortality, HRS, or AKI. The risks of drug‐related AE and fungal infections were similar in patients with AH who received prophylactic antibiotics or SMT. Using trial sequential analysis, the minimum sample size required to detect a 15% relative risk reduction in 90 days mortality with prophylactic antibiotics was 1171. Conclusions In hospitalized AH patients receiving steroid therapy, prophylactic antibiotics reduced the risk of infection and HE, but did not improve survival or prevent AKI compared to SMT.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".