Hepatocellular carcinoma for the non-specialist
Bibliographic record
Abstract
#### Summary points Hepatocellular carcinoma is the third most common cause of cancer related mortality worldwide, and in the United Kingdom population data show that age standardised incidence rose from 1.4 to 3.9 per 100 000 people between 1975 and 2006 (http://info.cancerresearchuk.org/cancerstats/types/liver). Cirrhosis of the liver is the strongest predisposing factor—80-90% of cases arise from chronic liver disease. Furthermore, in cohort studies of patients with cirrhosis, hepatocellular carcinoma is the leading cause of liver related death.1 2 #### Sources and selection criteria We based this review on the available evidence presented in international consensus guidelines and cited in PubMed after searching with the terms “hepatocellular carcinoma”, “natural history”, “surveillance”, “screening”, “outcome”, “treatment”, and “prevention”. Worldwide rates of hepatocellular carcinoma (fig 1⇓) correlate with widespread infection with hepatitis B in Asia and Africa and hepatitis C in Western countries and Japan. These viral infections are the most common underlying causes of liver disease that predispose to hepatocellular carcinoma (box 1). #### Box 1 Important risk factors for hepatocellular carcinoma Fig 1 2002 estimates of age standardised incidence of hepatocellular carcinoma. Incidence varies 14-fold across the world for men and 10-fold for women. The disease is still rare in the UK—140th of the 172 countries worldwide for men and 136th for women. Adapted, with permission, from Cancer Research UK Chronic …
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.254 | 0.121 |
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".