Low uptake of treatment for hepatitis C virus infection in a large community‐based study of inner city residents
Bibliographic record
Abstract
Despite the availability of effective therapy for hepatitis C virus (HCV) infection, there are little data on the uptake of treatment. We evaluated factors associated with HCV infection and the uptake of HCV treatment in a large community-based inner city cohort in Vancouver, Canada. The Community Health and Safety Evaluation is a cohort study of inner city residents recruited from January 2003 to June 2004. HIV and HCV status and information on prescriptions for HCV treatment were determined through linkage with provincial databases. HCV prevalence was calculated and factors associated with HCV infection were identified. HCV treatment uptake and incidence of HCV infection from January 2000 to December 2004 were expressed in terms of person-years of observation. Among 2913 individuals, HCV antibody testing was performed in 2118 and the HCV seroprevalence was 64.2% (1360 of 2118). In total, 1.1% of HCV antibody-positive individuals (15 of 1360) initiated treatment for HCV infection from January 2000 to December 2004 [0.28 cases per 100 person-years (95% CI, 0.15-0.46)]. Three of 15 (20.0%) treated individuals achieved a sustained virological response. During the same period, the incidence of HCV infection was 7.26 cases (95% CI, 5.72-8.80) per 100 person-years. Overall, the rate of new HCV seroconversions in this cohort in the study period was about 25 times the rate of HCV treatment uptake. There are extremely low rates of HCV treatment initiation and very limited effectiveness, despite a high prevalence of HCV infection in this large community-based cohort of inner city residents with access to universal healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".