Noninvasive assessment oesophageal varices: impact of the Baveno VI criteria
Bibliographic record
Abstract
PURPOSE OF REVIEW: In 2015, as a consequence of the high development in noninvasive tests, Baveno VI consensus recommended for the first time the use of a prediction rule (liver stiffness <20kPa and platelet count > 150000) to identify patients at low risk of having varices and that could circumvent endoscopy. These became known as the Baveno VI criteria. We review here the data validating Baveno VI criteria and we discuss the attempts of expanding these criteria. RECENT FINDINGS: We report 28 studies assessing the performance of Baveno VI criteria showing a pooled 99% negative predictive value for ruling out high-risk varices. Performance is not affected by the cause of cirrhosis. Different attempts at expanding these criteria show suboptimal performance. Nonelastography-based criteria require further validation. SUMMARY: Baveno VI criteria can be safely used to avoid endoscopy in a substantial proportion of patients with compensated cirrhosis. The progressive change in approach to the management of compensated cirrhosis, progressively focusing on treating portal hypertension with beta-blockers independently of the presence of varices, might render these criteria less relevant.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".