A27 CHARACTERIZATION OF HCV INFECTED PWID IN THE SETTING OF CLINICAL CARE IN CANADA (CAPICA): FINAL RESULTS
Bibliographic record
Abstract
HCV-related liver disease in people who inject drugs (PWIDs) carries a heavy personal, healthcare and societal burden. Current HCV treatment uptake in PWIDs is low and related to barriers at the individual, provider and healthcare system levels. Collect data related to demographic, medical and behavioral variables in HCV-infected PWIDs already engaged in care in Canada to help define treatment barriers. This multicenter observational study used retrospective chart review to collect data on patients receiving care from 12 Canadian centers. Patients with chronic HCV infection (HCV RNA+) and a history of injection drug use (in the previous 12 months) were included; HIV co-infection was excluded. Data were collected from October 2015 to February 2016. Of 423 participants: 74% were male, 65% Caucasian, 12% Aboriginal, with a median age of 42 years. All clinical sites provided multidisciplinary care and 11/12 had harm reduction programs. 33% of patients injected daily and 20% recently shared needles. Most frequent HCV genotypes were 1a (47%) and 3 (29%). When the fibrosis score was known (65% cases), 55% had F0-F1 and 14% had F4. The majority of patients were not yet being treated for HCV (83%). Of the 71 patients who received treatment, 37% (26/71) received IFN-free regimens. In the multivariate analysis, increasing age (OR = 1.10, 95% CI [1.03, 1.08]), not using a needle exchange program (OR = 6.95, 95% CI [1.73, 27.97]), moderate alcohol consumption (males ≤ 15 or females ≤ 10 drinks per week) vs. other (OR = 3.70, 95% CI [2.05,6.69]) and a recent fibrosis assessment with F4 vs. F0-F3 (OR = 4.91, 95% CI [2.18,11.09]) were associated with a higher likelihood of receiving treatment. A large number of HCV-infected PWIDs are engaged in care in Canada. Treatment rates are still low and patients are being prioritized for treatment. Barriers to treatment identified in this analysis will help to design targeted interventions for this group. Merck Canada Inc.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".