Comparison of liver cancer incidence and survival by subtypes across seven high‐income countries
Bibliographic record
Abstract
International comparison of liver cancer survival has been hampered due to varying standards and degrees for morphological verification and differences in coding practices. This article aims to compare liver cancer survival across the International Cancer Benchmarking Partnership's (ICBP) jurisdictions whilst trying to ensure that the estimates are comparable through a range of sensitivity analyses. Liver cancer incidence data from 21 jurisdictions in 7 countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the United Kingdom) were obtained from population-based registries for 1995-2014. Cases were categorised based on histological classification, age-groups, basis of diagnosis and calendar period. Age-standardised incidence rate (ASR) per 100 000 and net survival at 1 and 3 years after diagnosis were estimated. Liver cancer incidence rates increased over time across all ICBP jurisdictions, particularly for hepatocellular carcinoma (HCC) with the largest relative increase in the United Kingdom, increasing from 1.3 to 4.4 per 100 000 person-years between 1995 and 2014. Australia had the highest age-standardised 1-year and 3-year net survival for all liver cancers combined (48.7% and 28.1%, respectively) in the most recent calendar period, which was still true for morphologically verified tumours when making restrictions to ensure consistent coding and classification. Survival from liver cancers is poor in all countries. The incidence of HCC is increasing alongside the proportion of nonmicroscopically verified cases over time. Survival estimates for all liver tumours combined should be interpreted in this context. Care is needed to ensure that international comparisons are performed on appropriately comparable patients, with careful consideration of coding practice variations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".