Overcoming Hepatitis C: changes in quality of life, healthcare use and substance use in HIV-coinfected patients after antiviral therapy
Bibliographic record
Abstract
Background: In chronic hepatitis C virus (HCV) mono-infection and HIV/ HCV co-infection, the goal of antiviral treatment is a sustained virologic response (SVR). Some clinical benefits of SVR have been identified among HIV co-infected patients. However, endpoints beyond liver-related outcomes have not been well documented in co-infected patients who often have concurrent problems. We examined changes in health-related quality of life (HRQOL), health service use and substance over time among patients treated for HCV, in particular SVR-achievers and non-responders. Methods: HIV/ HCV co-infected patients with detectable HCV RNA were selected from the Canadian Co-infection Cohort and followed every six months. HRQOL was self-reported using the EuroQOL-5D visual analogue scale from 0 to 100 (worst to best health). Incidence rate ratios (IRR) for health service utilization and proportion of current users for substance use were determined. Linear and negative binomial regressions were used to model the effects of SVR on HRQOL and healthcare utilization respectively. Results:Of 1002 chronic HCV patients, 169 (17%) received treatment— 65 (38%) achieved SVR, 46 (27%) did not, 35 (21%) had ongoing treatment and 23 (14%) had unknown treatment response. EuroQOL scores improved in SVR-achievers after treatment (median (Q1, Q3): from 71 (60, 80) to 80 (70, 95.8)), but not in non-achievers (median (Q1, Q3): from 70 (48, 80) to 68 (50, 80)). Overall, SVR-achievers used fewer health services than non-achievers, particularly emergency visits and hospitalizations (IRR (95% CI): 0.36 (0.1, 1.0) and 0.17 (0.0, 0.5), respectively). One exception was walk-in clinic visits (IRR: 3.26 (95% CI: 1.3, 10.6)). Achieving SVR was associated with markedly decreased in-patient service use (IRR: 0.21 (95% CI: 0.07, 0.64). All Patients reduced tobacco smoking and illicit drug use behaviours, but alcohol consumption increased post-treatment among all patients (percentage reporting consumption: from 49% pre-treatment to 64% post-treatment in SVR-achievers; from 44% to 61% in non-achievers).Conclusions: HCV treatment and SVR can have a range of effects on HRQOL, healthcare use and substance use in HIV/ HCV co-infection. Longer follow-up is required to determine the duration of health benefits.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".