Determinants of Antiviral Treatment Initiation in a Hepatitis C-infected Population Benefiting from Universal Health Care Coverage
Bibliographic record
Abstract
BACKGROUND AND AIMS: In view of increasing therapeutic efficacy, the delivery of hepatitis C virus (HCV) antiviral treatment is expected to increase. Yet practical experience reveals a low rate of treatment, particularly among intravenous drug users. The aim of the present study was to examine the prevalence of HCV treatment and identify factors associated with HCV treatment in a population of patients evaluated in an academic hepatology outpatient clinic between 2001 and 2002. PATIENTS AND METHODS: The charts of HCV-infected patients who attended the outpatient clinic of the liver division between January 2001 and December 2002 were retrospectively reviewed. Regression analyses were conducted to compare patients according to HCV treatment initiation. RESULTS: Of 378 eligible patients (past intravenous drug users 61%), 143 (38%) initiated antiviral treatment. Enrolment in a methadone maintenance program and a strong willingness to get treatment were independently associated with treatment initiation, while current intravenous drug use, alcoholic liver damage on biopsy, precarious housing arrangements and personality disorders were negatively associated with treatment initiation. Among patients who were offered treatment, 40% refused (they did not differ from the treated group for past or current substance abuse). CONCLUSIONS: Only 38% of eligible patients initiated treatment; treatment refusal was very common. The results of the present study showed that a significant barrier to therapy involved patient perceptions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".