Validation of the Toronto hepatocellular carcinoma risk index for patients with cirrhosis in China: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The Toronto hepatocellular carcinoma (HCC) risk index (THRI) was developed to predict HCC in patients with cirrhosis. This study aimed to validate the THRI in a 10-year Asian cohort. METHODS: A total of 2836 patients with cirrhosis at the First Affiliated Hospital of Soochow University between January 2008 and May 2018 were evaluated. Based on the THRI value at diagnosis, patients were divided into three groups (< 120, low-risk; 120-240, intermediate-risk; > 240, high-risk). Student's t test and Fisher's exact test were applied to compare parameters between the HCC group and the non-HCC group. The receiver operator characteristic (ROC) curve was drafted to identify the value of the THRI in predicting HCC. Logistic regression was utilized to assess the relationship between the development of HCC and THRI values. The incidence of HCC was calculated for the three groups using the Kaplan-Meier method, and curves were compared using the log-rank test. RESULTS: Of 520 patients enrolled in this study, 76 patients developed HCC. Patients who developed HCC had a higher THRI score than those who did not develop HCC (279.5 ± 57.1 vs. 232.3 ± 67.6, respectively, p < 0.001). The area under the ROC curve for the THRI to predict HCC was 0.707 ([95% CI 0.645-0.769], p < 0.001), with a sensitivity of 0.842 and a specificity of 0.486 when the cutoff THRI value was 226. Compared to the low-risk group, the high-risk group presented higher odds of developing HCC (adjusting odds ratio 1.026 [95% CI 1.002-1.051], p = 0.036). Differences existed in the cumulative incidence of HCC among the three risk groups (log-rank, p < 0.001). The 5-year cumulative HCC incidence of the low-risk group, intermediate-risk group, and high-risk group was 0%, 13%, and 34%, respectively. CONCLUSION: This study validated THRI values for predicting HCC in Asians with cirrhosis, which presented a fine sensitivity to identify the high-risk population of HCC for secondary prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".