Diabetes Mellitus Heightens the Risk of Hepatocellular Carcinoma Except in Patients With Hepatitis C Cirrhosis
Bibliographic record
Abstract
OBJECTIVES: As most hepatocellular carcinoma (HCC) patients have cirrhosis, the association between diabetes and HCC may be confounded by the fact that diabetes is common in patients with cirrhosis. The aim of this study is to investigate whether diabetes increases the risk of HCC in patients with cirrhosis and whether the etiology of liver disease modifies the association between diabetes and HCC. METHODS: All liver cirrhosis patients who had repeated radiographic evaluation of the liver (that is, ultrasound, computed tomography, or magnetic resonance image) at Mayo Clinic Rochester between January 2006 and December 2011 were included. The Cox proportional hazard regression analysis was used to investigate the effect of diabetes on the risk of HCC. RESULTS: A total of 739 patients met the eligibility criteria, of whom 253 (34%) had diabetes. After a median follow-up of 38 months, 69 (9%) patients developed HCC. In patients without hepatitis C virus (HCV) infection, diabetes was significantly associated with the risk of developing HCC (hazard ratio (HR)=2.1, 95% confidence interval (CI)=1.1-4.1), whereas in patients with HCV, there was no association (HR=0.8, 95% CI=0.4-1.8). When adjusted for covariates, the interaction between HCV and diabetes remained significant (HR for non-HCV=1.9, 95% CI=0.9-3.7; HR for HCV=0.6, 95% CI=0.2-1.3). Lack of association between diabetes and HCC was externally validated in 410 patients with HCV cirrhosis enrolled in the HALT-C trial. CONCLUSIONS: Diabetes increases the risk of HCC in patients with non-HCV cirrhosis. In HCV cirrhosis patients who already have very high risk, diabetes may not increase the risk any further.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".