HCV infection characteristics, treatment uptake and outcomes in patient with diabetes mellitus
Bibliographic record
Abstract
Abstract Background The interplay between HCV, DM, and DAA therapy is poorly understood. We compared HCV infection characteristics, treatment uptake, and treatment outcomes in patients with and without DM. Methods A retrospective cohort study was conducted using data from The Ottawa Hospital Viral Hepatitis Program. Statistical comparisons between diabetes and non-diabetes were made using χ2 and t-tests. Logistic regression analyses were performed to assess predictors of DM and SVR. Results One thousand five hundred eighty-eight HCV patients were included in this analysis; 9.6% had DM. Patients with DM were older and more likely to have cirrhosis. HCC and chronic renal disease were more prevalent in the DM group. Treatment uptake and SVR were comparable between groups. Regression analysis revealed that age and employment were associated with achieving SVR. Post-SVR HCC was higher in DM group. Conclusion The high prevalence of DM in our HCV cohort supports screening. Further assessment is required to determine if targeted, early DAA treatment reduces DM onset, progression to cirrhosis and HCC risk. Further studies are needed to determine if optimization of glycemic control in this population can lead to improved liver outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".