Mortality in hepatitis C virus-cured vs. hepatitis C virus-uninfected people with HIV
Bibliographic record
Abstract
OBJECTIVE: It is unknown whether hepatitis C virus (HCV)-cured people with HIV (PWH) without cirrhosis reached the same mortality risk as HCV-uninfected PWH. We aimed to compare mortality in PWH cured of HCV by direct-acting antivirals (DAAs) to mortality in individuals with HIV monoinfection. DESIGN: Nationwide hospital cohort. METHODS: HIV-controlled participants without cirrhosis and HCV-cured by DAAs started between September 2013 and September 2020, were matched on age (±5 years), sex, HIV transmission group, AIDS status, and body mass index (BMI) (±1 kg/m 2 ) to up to 10 participants with a virally suppressed HIV monoinfection followed at the time of HCV cure ±6 months. Poisson regression models with robust variance estimates were used to compare mortality in both groups after adjusting for confounders. RESULTS: The analysis included 3961 HCV-cured PWH (G1) and 33 872 HCV-uninfected PWH (G2). Median follow-up was 3.7 years in G1 [interquartile range (IQR): 2.0-4.6], and 3.3 years (IQR: 1.7-4.4) in G2. Median age was 52.0 years (IQR: 47.0-56.0), and 29 116 (77.0%) were men. There were 150 deaths in G1 [adjusted incidence rate (aIR): 12.2/1000 person-years] and 509 (aIR: 6.3/1000 person-years) in G2, with an incidence rate ratio (IRR): 1.9 [95% confidence interval (CI), 1.4-2.7]. The risk remained elevated 12 months post HCV cure (IRR: 2.4 [95% CI, 1.6-3.5]). Non-AIDS/non-liver-related malignancy was the most common cause of death in G1 (28 deaths). CONCLUSIONS: Despite HCV cure and HIV viral suppression, after controlling on factors related to mortality, DAA-cured PWH without cirrhosis remain at higher risk of all-cause mortality than people with HIV monoinfection. A better understanding of the determinants of mortality is needed in this population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".