Heterogeneity of treatment response to beta-blockers in the treatment of portal hypertension: A systematic review
Bibliographic record
Abstract
BACKGROUND: It has been suggested that a relevant proportion of patients do not respond to nonselective beta-blockers (NSBB)s, which raises questions regarding the need for individualized therapy. The existence of potential heterogeneity in the treatment response can be assessed using the variability ratio (VR) of the outcome measurement (in this case, HVPG) between the treated and placebo groups. We conducted a systematic review and meta-analysis of randomized controlled trials to assess the potential heterogeneity in the portal pressure response to NSBBs. METHODS: After a systematic search, we quantified the heterogeneity of treatment response with the VR between the treatment and control groups, with VR > 1 indicating potential heterogeneity. We used a similar approach to compare carvedilol with propranolol and statins with placebo. RESULTS: We identified 18 studies that included 965 patients. A comparison between beta-blockers and placebo showed a pooled VR of 0.99 (95% CI:0.87-1.14), which suggests a homogeneous HVPG response to NSBB at the individual patient level (ie, no evidence to support that some patients responded to beta-blockers and others did not). For the comparison between carvedilol and propranolol, pooled VR was 0.97 (95% CI 0.82-1.14), suggesting that carvedilol achieves a greater average response (rather than an increase in the proportion of responders). There was no evidence of a heterogeneous response to statins. CONCLUSION: Our analysis did not support the existence of a heterogeneous patient-by-patient response to NSBBs in cirrhosis. These findings challenge the concept of personalized therapy based on portal pressure response and indicate that routine portal pressure measurement may not be necessary to guide NSBB therapy.
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".