Noninvasive tools and risk of clinically significant portal hypertension and varices in compensated cirrhosis: The “Anticipate” study
Bibliographic record
Abstract
In patients with compensated advanced chronic liver disease (cACLD), the presence of clinically significant portal hypertension (CSPH) and varices needing treatment (VNT) bears prognostic and therapeutic implications. Our aim was to develop noninvasive tests-based risk prediction models to provide a point-of-care risk assessment of cACLD patients. We analyzed 518 patients with cACLD from five centers in Europe/Canada with paired noninvasive tests (liver stiffness measurement [LSM] by transient elastography, platelet count, and spleen diameter with calculation of liver stiffness to spleen/platelet score [LSPS] score and platelet-spleen ratio [PSR]) and endoscopy/hepatic venous pressure gradient measurement. Risk of CSPH, varices, and VNT was modeled with logistic regression. All noninvasive tests reliably identified patients with high risk of CSPH, and LSPS had the highest discrimination. LSPS values above 2.65 were associated with risks of CSPH above 80%. None of the tests identified patients with very low risk of all-size varices, but both LSPS and a model combining TE and platelet count identified patients with very low risk (<5%) risk of VNT, suggesting that they could be used to triage patients requiring screening endoscopy. LSPS values of <1.33 were associated with a <5% risk of VNT, and 26% of patients had values below this threshold. LSM combined with platelet count predicted a risk <5% of VNT in 30% of the patients. Nomograms were developed to facilitate point-of-care risk assessment. CONCLUSION: A significant proportion of patients with a very high risk of CSPH, and a population with a very low risk of VNT can be identified with simple, noninvasive tests, suggesting that these can be used to individualize medical care. (Hepatology 2016;64:2173-2184).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".