External validation of the Toronto hepatocellular carcinoma risk index in Turkish cirrhotic patients
Bibliographic record
Abstract
OBJECTIVES: Toronto hepatocellular carcinoma risk index is developed to stratify cirrhotic patients according to 10-year hepatocellular carcinoma risk. We aimed to validate the performance of Toronto hepatocellular carcinoma risk index in a large Turkish cohort. MATERIALS AND METHODS: We retrospectively reviewed the database of 1287 cirrhotic patients followed-up in a 10-year period (February 2008 to January 2018). All patients were stratified into three groups based on the Toronto hepatocellular carcinoma risk index score as follows: low-risk, < 120; intermediate risk, 120 to 240; and high risk, > 240. Area under the curve and optimal cutoff value of Toronto hepatocellular carcinoma risk index were obtained from receiver operator curve. To reveal the parameters related with hepatocellular carcinoma development, logistic regression analysis was conducted. The cumulative incidences of hepatocellular carcinoma were calculated using the Kaplan-Meier method, and the curves were compared using the log-rank test. RESULTS: Out of 403 enrolled patients, 57 developed hepatocellular carcinoma. The median Toronto hepatocellular carcinoma risk index value was higher in hepatocellular carcinoma (+) group comparing to hepatocellular carcinoma (-) group [267 (70-366) vs. 224 (36-366), P < 0.001]. Out of 57 detected hepatocellular carcinomas, 45 (78.9%) were high risk, 11 (19.3%) were intermediate risk, and only one (1.8%) was low risk at the entry. The area under the curve of the Toronto hepatocellular carcinoma risk index to predict hepatocellular carcinoma was 0.750 (95% confidence interval, 0.683-0.817, P < 0.001). The optimal cutoff value of Toronto hepatocellular carcinoma risk index was 239.5, giving a sensitivity of 78.9% and specificity of 62.7%. As a result, Toronto hepatocellular carcinoma risk index remained to be the only significant parameter that has an affect on hepatocellular carcinoma development [adjusted-odds ratio: 1.016 (95% confidence interval, 1.007-1.024), P<0.001]. CONCLUSION: The present study validated the performance of Toronto hepatocellular carcinoma risk index in Turkish cirrhotic patients to predict hepatocellular carcinoma risk, which can be considered as a tool for personalized surveillance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".