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 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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".