Treatment of hepatitis C infection for current or former substance abusers in a community setting
Bibliographic record
Abstract
Substance abusers account for the largest number of hepatitis C infected cases in developed countries. We describe a care model for treating current or former substance abusers with antiviral therapy for hepatitis C virus (HCV) infection. The care model involved hepatitis nurses, a psychologist, infectious disease specialist and primary care physicians. Clients met selection criteria including regular attendance at clinic appointments and social stability. Use of alcohol and illicit substances was monitored with urine toxicology screens. The association between substance use, rates of completion of therapy and rates of response were assessed using multivariable regression analyses. A total of 109 clients (75 with genotype 1/4 and 34 with genotype 2/3) received at least one injection with pegylated interferon between November 2002 and January 2006. Treatment completion rates of 61 and 74% were achieved for genotypes 1/4 and 2/3, respectively. Treatment response rates in an intention to treat analysis were 51% for genotypes 1/4 and 68% for genotypes 2/3. A positive urine toxicology screen indicating use of illicit substances 6 months prior to initiating therapy was significantly associated with lower rates of treatment completion but not lower rates of sustained virological response. A positive urine screen indicating use of alcohol prior to therapy was significantly associated with lower rates of completion and lower rates of response. Rates of completion and response are comparable to non-substance abusing populations. Antiviral therapy for HCV infection can be successful within the context of ongoing care for substance abuse for carefully selected patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".