Trends in cause-specific mortality in HIV–hepatitis C coinfection following hepatitis C treatment scale-up
Bibliographic record
Abstract
OBJECTIVE: Hepatitis C virus (HCV) treatment may reduce liver-related mortality but with competing risks, other causes of mortality may undermine benefits. We examined changes in cause-specific mortality among HIV-HCV coinfected patients before and after scale-up of HCV treatment. DESIGN: Prospective multicentre HIV-HCV cohort study in Canada. METHODS: Cause-specific deaths, classified using a modified 'Coding of Cause of Death in HIV' protocol, were determined for two time periods, 2003-2012 and 2013-2017, stratified by age (20-49; 50-80 years). Comparison of trends between periods was performed using Poisson regression. To account for competing risks, multinomial regression was used to estimate the cause-specific hazard ratios of time and age on cause of death, from which end-stage liver disease (ESLD)-specific 5-year cumulative incidence functions were estimated. RESULTS: Overall, 1634 participants contributed 8248 person-years of follow-up; 273 (17%) died. Drug overdose was the most common cause of death overall, followed by ESLD and smoking-related deaths. In 2013-2017, ESLD was surpassed by drug overdose and smoking-related deaths among those aged 20-49 and 50-80, respectively. After accounting for competing risks, comparing 2003-2012 to 2013-2017, ESLD deaths declined (adjusted hazards ratio: 0.18, 95% confidence interval 0.05-0.62). However, both early and late period cumulative incidence functions demonstrated increased risk of death from ESLD for patients with poor HIV control and advanced fibrosis. CONCLUSION: The gains made in overall mortality with HCV therapy may be thwarted if modifiable harms are not addressed. Although ESLD-related deaths have decreased over time, treatment should be further expanded, prioritizing those with advanced fibrosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".