Meta‐Analysis: Utilisation of Hepatocellular Carcinoma Surveillance
Bibliographic record
Abstract
BACKGROUND AND AIMS: Major society guidelines recommend hepatocellular carcinoma (HCC) surveillance every 6 months with hepatic ultrasound. HCC surveillance is associated with early detection and improved survival but is underutilised. We aim to provide an updated assessment of HCC surveillance utilisation. METHODS: Ovid MEDLINE and Embase were searched from inception until 30 November 2023 to identify studies reporting the proportion of people with either cirrhosis or chronic hepatitis B who underwent ultrasound-based HCC surveillance. The primary objective was to determine the utilisation of HCC surveillance. A meta-analysis of proportions was conducted using a generalised linear mixed model. RESULTS: Forty-eight articles (1,275,349 individuals) met inclusion criteria. In at-risk individuals (cirrhosis or chronic hepatitis B), pooled utilisation of any HCC surveillance (n = 21 studies) was 54.45% (95% CI: 37.77-70.19), and utilisation of biannual surveillance (n = 7 studies) was 8.76% (95% CI: 2.46-26.79). Utilisation of any HCC surveillance was 53.37% (95% CI: 33.72-72.03) in patients with cirrhosis (n = 15 studies) and 66.43% (95% CI: 42.74-83.99) in patients with chronic hepatitis B (n = 12 studies), while utilisation of biannual surveillance was 10.20% (95% CI: 1.92-39.71) and 12.96% (95% CI: 0.02-99.09) respectively. Utilisation of surveillance did not improve over time. Pooled analysis of 40,497 individuals diagnosed with HCC (n = 27 studies) determined that the proportion of patients who had undergone prior screening or been diagnosed by surveillance was 36.07% (95% CI: 28.30-44.63). CONCLUSIONS: Less than 10% of patients received the recommended biannual ultrasound scans for surveillance. These findings are concerning and call for greater awareness and collaboration between care providers and healthcare policymakers to improve surveillance utilisation.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".