Comparing serial and current liver stiffness measurements to predict decompensation in compensated advanced chronic liver disease patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: The utility of serial liver stiffness measurements (LSM) to predict decompensation in patients with compensated advanced chronic liver disease (cACLD) remains unclear. We aimed to validate whether comparing serial LSM is superior to using the current LSM to predict liver-related events (LRE) in patients with cACLD. APPROACH AND RESULTS: In this retrospective analysis of an international registry, patients with cACLD and serial LSM were followed up until index LRE. We compared the performance of both the dynamic LSM changes and the current LSM in predicting LRE using Cox regression analysis, considering time zero of follow-up as the date of latest liver stiffness measurement. Overall, 480 patients with cACLD with serial LSM were included from 5 countries. The commonest etiology of cACLD was viral (53%) and MASLD (34%). Over a median follow-up of 68 (IQR: 45-92) months, 32% experienced a LSM decrease to levels below 10kPa (resolved cACLD) and 5.8% experienced LRE. Resolved cACLD were more likely to be non-diabetic and had better liver function. While a higher value of the current LSM was associated with higher LREs, LSM changes over time (LSM slope) were not associated with LRE. In multivariable Cox regression, neither the prior LSM nor the LSM slope added predictive value to latest liver stiffness measurement. CONCLUSIONS: Once the current LSM is known, previous LSM values do not add to the prediction of LREs in patients with cACLD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".