aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis
Bibliographic record
Abstract
BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is the leading cause of death in patients with chronic hepatitis. In this international collaboration, we sought to develop a global universal HCC risk score to predict the HCC development for patients with chronic hepatitis. METHODS: A total of 17,374 patients, comprising 10,578 treated Asian patients with chronic hepatitis B (CHB), 2,510 treated Caucasian patients with CHB, 3,566 treated patients with hepatitis C virus (including 2,489 patients with cirrhosis achieving a sustained virological response) and 720 patients with non-viral hepatitis (NVH) from 11 international prospective observational cohorts or randomised controlled trials, were divided into a training cohort (3,688 Asian patients with CHB) and 9 validation cohorts with different aetiologies and ethnicities (n = 13,686). RESULTS: We developed an HCC risk score, called the aMAP score (ranging from 0 to 100), that involves only age, male, albumin-bilirubin and platelets. This metric performed excellently in assessing HCC risk not only in patients with hepatitis of different aetiologies, but also in those with different ethnicities (C-index: 0.82-0.87). Cut-off values of 50 and 60 were best for discriminating HCC risk. The 3- or 5-year cumulative incidences of HCC were 0-0.8%, 1.5-4.8%, and 8.1-19.9% in the low- (n = 7,413, 43.6%), medium- (n = 6,529, 38.4%), and high-risk (n = 3,044, 17.9%) groups, respectively. The cut-off value of 50 was associated with a sensitivity of 85.7-100% and a negative predictive value of 99.3-100%. The cut-off value of 60 resulted in a specificity of 56.6-95.8% and a positive predictive value of 6.6-15.7%. CONCLUSIONS: This objective, simple, reliable risk score based on 5 common parameters accurately predicted HCC development, regardless of aetiology and ethnicity, which could help to establish a risk score-guided HCC surveillance strategy worldwide. LAY SUMMARY: In this international collaboration, we developed and externally validated a simple, objective and accurate prognostic tool (called the aMAP score), that involves only age, male, albumin-bilirubin and platelets. The aMAP score (ranged from 0 to 100) satisfactorily predicted the risk of hepatocellular carcinoma (HCC) development among over 17,000 patients with viral and non-viral hepatitis from 11 global prospective studies. Our findings show that the aMAP score had excellent discrimination and calibration in assessing the 5-year HCC risk among all the cohorts irrespective of aetiology and ethnicity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".