Quantifying the benefit of nonselective beta-blockers in the prevention of hepatic decompensation: A Bayesian reanalysis of the PREDESCI trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Beta-blockers have been studied for the prevention of variceal bleeding and, more recently, for the prevention of all-cause decompensation. Some uncertainties regarding the benefit of beta-blockers for the prevention of decompensation remain. Bayesian analyses enhance the interpretation of trials. The purpose of this study was to provide clinically meaningful estimates of both the probability and magnitude of the benefit of beta-blocker treatment across a range of patient types. APPROACH AND RESULTS: We undertook a Bayesian reanalysis of PREDESCI incorporating 3 priors (moderate neutral, moderate optimistic, and weak pessimistic). The probability of clinical benefit was assessed considering the prevention of all-cause decompensation. Microsimulation analyses were done to determine the magnitude of the benefit. In the Bayesian analysis, the probability that beta-blockers reduce all-cause decompensation was >0.93 for all priors. The Bayesian posterior hazard ratios (HR) for decompensation ranged from 0.50 (optimistic prior, 95% credible interval 0.27-0.93) to 0.70 (neutral prior, 95% credible interval 0.44-1.12). Exploring the benefit of treatment using microsimulation highlights substantial treatment benefits. For the neutral prior derived posterior HR and a 5% annual incidence of decompensation, at 10 years, an average of 497 decompensation-free years per 1000 patients were gained with treatment. In contrast, at 10 years 1639 years per 1000 patients were gained from the optimistic prior derived posterior HR and a 10% incidence of decompensation. CONCLUSIONS: Beta-blocker treatment is associated with a high probability of clinical benefit. This likely translates to a substantial gain in decompensation-free life years at the population level.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".