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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".