Marijuana Use in Hepatitis C Infection does not Affect Liver Biopsy Histology or Treatment Outcomes
Bibliographic record
Abstract
BACKGROUND: Marijuana smoking is prevalent among hepatitis C virus-infected patients. The literature assessing the influence of marijuana on liver disease progression and hepatitis C virus antiviral treatment outcomes is conflicting. METHODS: The authors evaluated hepatitis C virus RNA-positive patients followed at The Ottawa Hospital Viral Hepatitis Clinic (Ottawa, Ontario) from 2000 to 2009. Using The Ottawa Hospital Viral Hepatitis Clinic database and charts, information regarding demographics, HIV coinfection, alcohol use, liver biopsy results, treatment outcomes and self-reported marijuana use was extracted. Biopsy characteristics and hepatitis C virus antiviral treatment outcomes were assessed for association with categorized marijuana use by adjusted logistic regression; covariates were specified according to clinical relevance a priori. RESULTS: Information regarding marijuana use was available for 550 patients, 159 (28.9%) of whom were using marijuana at the time of first assessment. Biopsy fibrosis stage and marijuana use data were available for 377 of these 550 (F0-2 = 72.3%). Overall, marijuana use did not predict fibrosis stage, inflammation grade or steatosis. Sustained virological response and marijuana use data were available for 359 of the 550 cohort participants; a total of 211 (58.8%) achieved a sustained virological response. Marijuana use was not associated with premature interruption of therapy for side effects, the likelihood of completing a full course of therapy or sustained virological response. CONCLUSION: Marijuana use did not influence biopsy histology or alter key hard outcomes of hepatitis C virus antiviral therapy.
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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 | 0.007 |
| 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 | 0.001 | 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".