History of alcohol use does not predict HCV direct acting antiviral treatment outcomes
Bibliographic record
Abstract
BACkGROUND: Hepatitis C virus (HCV) infection and excessive alcohol consumption are leading causes of liver disease worldwide. Direct acting antivirals (DAAs) are well-tolerated treatments for HCV infections with high sustained virologic response (SVR) rates. There are limited data assessing the influence of alcohol use on DAA uptake and cure. METHODS: We performed a retrospective analysis of patients followed at The Ottawa Hospital Viral Hepatitis Program between January 2014 and May 2020 to investigate the effect of excessive alcohol use history on DAA uptake and SVR rates. Additionally, we evaluated the incidence of concurrent comorbidities and social determinants of health. Predictors of DAA uptake and SVR were assessed by logistic regression. RESULTS: Excessive alcohol use history was reported in 46.0% (733) of patients. Excessive alcohol use did not predict DAA uptake (OR 1.06, 95% CI 0.71 to 1.57), while employment (OR 2.10, 95% CI 1.29 to 3.42) and recreational drug use (OR 0.62, 95% CI 0.40 to 0.94) were predictors. Employment predicted SVR (OR 2.38, 95% CI 1.68 to 3.36) in those starting treatment. Excessive alcohol use history did not predict SVR. CONCLUSIONS: History of excessive alcohol use does not influence treatment initiation or SVR. Efforts to improve treatment uptake should shift to focus on the roles of determinants of health such as employment and recreational drug use on treatment initiation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".