Dedicated Automatic Recall Hepatocellular Cancer Surveillance Programme Demonstrates High Retention: A Population‐Based Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Patient, clinician, and system-related barriers may affect adherence to hepatocellular carcinoma (HCC) surveillance programmes. The impact of a dedicated automated recall HCC surveillance programme on retention rates in patients eligible for screening is unknown. We aimed to describe and evaluate a large HCC surveillance programme in a publicly funded healthcare system. METHODS: Data were collected from January 1, 2013, to December 31, 2022, from a retrospective cohort of subjects enrolled in a publicly funded automated recall semi-annual surveillance programme as per the American Association for the Study of Liver Disease HCC guidance in the Calgary Health Zone (~1.6 million), Canada. Patients were excluded if there was incomplete data or did not meet indications for surveillance. Cox regression was used to identify predictors of non-retention to surveillance. RESULTS: A total of 7269 patients were included. The median was age 55.5 years (IQR: 45.5-63.8), 60% were male, 46% were of Asian descent, 51% had HBV infection, and 36% had cirrhosis (35% alcohol-related). Median follow-up was 4.9 years (IQR: 1.5-7.2). Overall, 52% (n = 3768) of patients were retained in the surveillance programme, while 8.3% (n = 603) left for potential medical reasons, and 40% (n = 2898) were lost in follow-up. The median time in the programme for those lost in follow-up was 0.81 years (IQR: 0.0-2.8) compared to 6.75 years if retained (IQR: 5.6-8.6; p < 0.001). In multivariable Cox regression analysis, HCV aetiology (HR 1.41; CI 1.23-1.62, p < 0.01), African ethnicity (HR 1.20, CI 1.02-1.42, p = 0.03), and cirrhosis (HR 1.16, CI 1.05-1.28, p < 0.01) increased risk of dropout. On interaction analysis, Hepatitis B amongst cirrhotic patients also increased risk of dropout (HR 1.48, CI 1.05-2.07, p = 0.02). CONCLUSION: A dedicated automated recall HCC surveillance programme has a high retention rate in a large multi-ethnic cohort of patients while identifying certain marginalised patient populations, such as those with viral liver disease, cirrhosis, or African ethnicity, as particularly vulnerable to loss to follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".