Risk prediction of hepatocellular carcinoma in patients with cirrhosis: The ADRESS‐HCC risk model
Bibliographic record
Abstract
BACKGROUND: All patients with cirrhosis are at risk of developing hepatocellular carcinoma (HCC). This risk is not uniform because other patient-related factors influence the risk of HCC. The objective of the current study was to develop an HCC risk prediction model to estimate the 1-year probability of HCC to assist with patient counseling. METHODS: Between 2002 and 2011, a cohort of 34,932 patients with cirrhosis was identified from a national liver transplantation waitlist database from the United States. Cox proportional hazards regression methods were used to develop and validate a risk prediction model for incident HCC. In the validation cohort, discrimination and calibration of the model was examined. External validation was conducted using patients with cirrhosis who were enrolled in the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) study. RESULTS: HCC developed in 1960 patients (5.6%) during a median follow-up of 1.3 years (interquartile range, 0.47 years-2.83 years). Six baseline clinical variables, including age, diabetes, race, etiology of cirrhosis, sex, and severity (ADRESS) of liver dysfunction were independently associated with HCC and were used to develop the ADRESS-HCC risk model. C-indices in the derivation and internal validation cohorts were 0.704 and 0.691, respectively. In the validation cohort, the predicted cumulative incidence of HCC by the ADRESS-HCC model closely matched the observed data. In patients with cirrhosis in the HALT-C cohort, the model stratified patients correctly according to the risk of developing HCC within 5 years. CONCLUSIONS: The ADRESS-HCC risk model is a useful tool for predicting the 1-year risk of HCC among patients with cirrhosis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".