Characterization of HCV-infected people who inject drugs (PWID) in the setting of clinical care in Canada (CAPICA): A retrospective study
Bibliographic record
Abstract
Background: People who use drugs (PWUD) are among the highest risk category for becoming infected with the hepatitis C virus (HCV) in Canada. There is a need for more information on the demographics of HCV-infected PWUD/PWID who have recently injected drugs or who are actively injecting drugs. Methods: CAPICA was a multicentre, retrospective database/chart review conducted from October 2015 to February 2016 that was designed to characterize HCV-infected people who inject drugs (PWID) and are enrolled in clinical care in Canada. The aim was to identify factors of health care engagement essential in the design systems of HCV care and treatment in this population. The study enrolled 420 patients with a history of injection drug use within the last 12 months who had been diagnosed with chronic viremic HCV infection and had been participants in an outpatient clinical care setting in the past 12 months. Patients who were co-infected with HIV/HCV were excluded. Results: Harm reduction programs were in place at 92% (11/12) of the sites, and 75% (9) of these sites offered opioid agonist therapy (OAT), with 48% of the patients currently taking OAT. HCV genotype 1a was most prevalent (56%), followed by G3 (34%), and the most common fibrosis score was F1 (34%). The average reinfection rate was about 5%. Seventeen percent of the patients were undergoing HCV treatment or had recently failed therapy, while 83% were not being treated. Conclusions: In a multivariate analysis, the following factors were significantly associated with treatment: increasing age (OR 1.10), a fibrosis score of F4 (OR 4.91), moderate alcohol consumption (OR 3.70), and not using a needle exchange program (OR 6.95).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".