A novel program for treating patients with trimorbidity
Bibliographic record
Abstract
BACKGROUND: Advances in hepatitis C virus (HCV) treatment have yielded improved virological response rates, and yet, many individuals with psychiatric illness still fail to receive HCV therapy. Concerns about safety, adherence, and efficacy of HCV treatment are compounded and treatment is further deferred when substance use is also present. This is especially problematic given the disproportionately high rates of both mental health issues and substance use among individuals living with HCV. OBJECTIVE: This study sought to examine HCV treatment outcomes in clients with serious mental illness (SMI) and with high rates of active substance use who were participating in a community-based HCV treatment program. PATIENTS AND METHODS: A retrospective chart review of 129 clients was carried out. Patients were classified as having an SMI if they had a history of bipolar disorder, psychotic disorder, past suicide attempt or mental health related hospitalization. RESULTS: Fifty-one patients were defined as having an SMI. Among the 46 patients with SMI and a detectable HCV viral load, HCV antiviral therapy was initiated in nine (19.6%). A relapse or an increase in substance use was common (77.8% or n=7), as was the requirement for adjustment or initiation of psychotropic medications (66.7% or n=6) during HCV antiviral therapy. Despite these barriers, rates of adherence to antiviral therapy were high and overall sustained virological response rates were comparable with published trials. CONCLUSION: This study is the first to report HCV treatment outcomes in a population in which SMI and active polysubstance use was prevalent and suggests that with appropriate models of care, clients with trimorbidity can be treated safely and effectively.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".