The hepatitis C virus cascade of care in a Quebec provincial prison: a retrospective cohort study
Bibliographic record
Abstract
Background: Hepatitis C virus (HCV) microelimination efforts must target people in prison; however, although some inmates may qualify for treatment in provincial prisons, it may not be routinely provided. Our aim was to characterize the cascade of HCV care in Quebec’s largest provincial prison. Methods: We conducted a retrospective study of all HCV-related laboratory tests requested at the Établissement de détention de Montréal (men’s prison with on-demand screening), between July 1, 2017, and June 30, 2018. We defined 8 HCV care cascade steps: 1) total sentenced inmates, 2) screened for HCV (via HCV antibody [HCV Ab]), 3) HCV Ab positive, 4) tested for HCV RNA, 5) HCV RNA positive, 6) linked to care, 7) HCV treatment initiated and 8) achieved sustained virologic response. We measured proportions of inmates at each step using denominator–numerator linkage. We also calculated the proportion screened among inmates with a sentence duration of at least 1 month, during which time screening should be feasible. Results: Of the 4931 sentenced inmates, 344 (7%) were screened for HCV, of whom 38 (11%) were HCV Ab positive. Thirty-five (92%) of the 38 received HCV RNA testing, which showed positivity in 16 (46%). Ten (62%) of the 16 inmates were linked to care; treatment was initiated in 3 (30%), 2 of whom (67%) achieved a sustained virologic response. Among inmates with a sentence duration of at least 1 month (n = 1972), the proportion screened increased to 17%. Interpretation: A small proportion (7%) of men at a Canadian provincial prison with on-demand HCV testing were screened, and rates of treatment initiation were low in the absence of formal HCV cure pathways. To eliminate HCV in this subpopulation, opt-out HCV testing should be considered.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".