Improved Survival in Patients with Viral Hepatitis-Induced Hepatocellular Carcinoma Undergoing Recommended Abdominal Ultrasound Surveillance in Ontario: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
The optimal schedule for ultrasonographic surveillance of patients with viral hepatitis for the detection of hepatocellular carcinoma (HCC) remains unclear owing to a lack of reliable studies. We examined the timing of ultrasonography in patients with viral hepatitis-induced HCC and its impact on survival and mortality risk while determining predictors of receiving surveillance before HCC diagnosis. A population-based retrospective cohort analysis of patients with viral hepatitis-induced HCC in Ontario between 2000 and 2010 was performed using data from the Ontario Cancer Registry linked health administrative data. HCC surveillance for 2 years preceding diagnosis was assigned as: i) ≥ 2 abdominal ultrasound screens annually; ii) 1 screen annually; iii) inconsistent screening; and iv) no screening. Survival rates were estimated using the Kaplan-Meier method and parametric models to correct for lead-time bias. Associations between HCC surveillance and the risk of mortality after diagnosis were examined using proportional-hazards regression adjusting for confounding factors. Overall, 1,483 patients with viral hepatitis-induced HCC were identified during the study period; 20.2% received ≥ 1 ultrasound screen annually (routine surveillance) for the 2 years preceding diagnosis. The 5-year survival of those receiving routine surveillance was 31.93% (95% CI: 25.77-38.24%) and 31.84% (95% CI: 25.69-38.14%) when corrected for lead-time bias (HCC sojourn time 70 days and 140 days, respectively). This is contrasted with 20.67% (95% CI: 16.86-24.74%) 5-year survival in those who did not undergo screening. In the fully adjusted model, compared to unscreened patients, routine surveillance was associated with a lower mortality risk and a hazard ratio of 0.76 (95% CI: 0.64-0.91) and 0.81 (95% CI: 0.68-0.97), corrected for the respective lead-time bias. Our findings suggest that routine ultrasonography in patients with viral hepatitis is associated with improved survival and reduced mortality risk in a population-based setting. The data emphasizes the importance of surveillance for timely intervention in HCC-diagnosed patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".