Hepatitis C and diabetes: one treatment for two diseases?
Bibliographic record
Abstract
Abstract Epidemiological data clearly indicate a link between chronic hepatitis C (CHC) and disturbed glucose homeostasis. The prevalences of both type 2 diabetes mellitus (T2DM) and insulin resistance (IR) are higher among those chronically infected with hepatitis C when compared with the general population and those with other causes of chronic liver disease. Both IR and diabetes are associated with adverse outcomes across all stages of CHC including the liver transplant population. The adverse effects that directly influence patient outcome are reduced responsiveness to antiviral therapy, more rapid progression of fibrosis to cirrhosis and a higher incidence of hepatocellular carcinoma. Although both viral and host factors are known to contribute to IR (and therefore the risk of T2DM), there is a paucity of evidence to support interventions targeting IR with pharmacotherapy or lifestyle intervention. The purpose of this review is to examine the impact of abnormalities of glucose homeostasis in CHC, and in so doing, to raise a number of questions. How do we identify those at risk of diabetes in CHC? Can we reduce the incidence of hepatoma and reduce transplant-related morbidity and mortality by preventing or treating diabetes? Can we improve the response to antiviral therapy by pretreating IR and T2DM in treatment candidates? Ultimately, can we cure two diseases, diabetes and CHC, with one treatment?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".