Mortality in HIV–hepatitis C co-infected patients in Canada compared to the general Canadian population (2003–2013)
Bibliographic record
Abstract
OBJECTIVE: Recent studies suggest all-cause mortality in HIV mono-infected patients approaches that of the general population. We aimed to compare participants in the Canadian Co-infection Cohort to the general population to determine if co-infected patients have had similar improvements in mortality. DESIGN: Prospective multicentre cohort study. METHODS: Between 2003 and 2013, deaths were captured using specific case reports and through linkage to provincial vital statistics for participants lost to follow-up. Standardized mortality ratios (SMRs) were calculated using age, sex and province-specific mortality rates from the Canadian Human Mortality Database, 2009, and compared across behavioural and clinical characteristics of participants at their most recent visit. RESULTS: Among the 1150 patients, we observed 133 deaths over 3351 person-years (4.0 per 100 person-years, 95% confidence interval 3.3, 4.6). SMRs (95% confidence interval) were: 12.1(10.1, 14.2) overall; 9.3 (7.5, 11.1) for men and 19.4 (12.7, 26.2) for women. CD4 cell counts below 200 cells/μl [25.5 (17.7, 33.3)], active injection drug use [19.9 (13.9, 25.9)] and smoking [14.9 (12.1, 17.7)] were strongly associated with excess mortality. Lowest SMRs were seen for those who had spontaneous [4.5 (-0.6, 9.5)] or treatment-induced clearance of hepatitis C virus (HCV) infection [5.1 (1.3, 8.8)]. Conversely, high SMRs were seen with advanced liver disease [17.0 (11.7, 22.3)]. In no category did SMRs approach mortality seen in the general Canadian population. CONCLUSIONS: HIV-HCV co-infected persons remain at markedly increased risk for death despite antiretroviral therapy. Interventions targeting modifiable risk factors such as substance use, smoking, adherence to antiretrovirals and timely provision of HCV therapy could substantially reduce death rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".