Effect of treatment willingness on specialist assessment and treatment uptake for hepatitis C virus infection among people who use drugs: the <scp>ETHOS</scp> study
Bibliographic record
Abstract
Among people who inject drugs (PWID) with chronic HCV, the association between HCV treatment willingness and intent, and HCV specialist assessment and treatment were evaluated. The Enhancing Treatment for Hepatitis C in Opioid Substitution Settings (ETHOS) is a prospective observational cohort. Recruitment was through six opioid substitution treatment clinics, two community health centres and one Aboriginal community controlled health organisation in Australia. Analyses were performed using logistic regression. Among 415 participants (mean age 41 years, 71% male), 67% were 'definitely willing' to receive HCV treatment and 70% reported plans to initiate therapy 12 months postenrolment. Those definitely willing to receive HCV treatment were more likely to undergo specialist assessment (64% vs 32%, P < 0.001) and initiate therapy (36% vs 9%, P < 0.001), compared to those with lower treatment willingness. Those with early HCV treatment plans were more likely to undergo specialist assessment (65% vs 27%, P < 0.001) and initiate therapy (36% vs 5%, P < 0.001), compared to those without early plans. In adjusted analyses, HCV treatment willingness independently predicted specialist assessment (aOR 3.06, 95% CI 1.90, 4.94) and treatment uptake (aOR 4.33, 95% CI 2.14, 8.76). In adjusted analysis, having early HCV treatment plans independently predicted specialist assessment (aOR 4.38, 95% CI 2.63, 7.29) and treatment uptake (aOR 9.79, 95% CI 3.70, 25.93). HCV treatment willingness was high and predicted specialist assessment and treatment. Strategies for enhanced HCV care should be developed with an initial focus on people willing to receive treatment and to increase treatment willingness among those less willing.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".