Clinical Outcomes in Persons Coinfected With Human Immunodeficiency Virus and Hepatitis C Virus: Impact of Hepatitis C Virus Treatment
Bibliographic record
Abstract
BACKGROUND: A hepatitis C (HCV) cure is associated with changes in lipids and inflammatory biomarkers, but its impact on clinical endpoints among treated human immunodeficiency virus (HIV)/HCV coinfected persons is unclear. METHODS: People living with HIV from EuroSIDA with a known HCV status after January 2001 were classified into strata based on time-updated HCV RNA measurements and HCV treatment, as either HCV antibody-negative; spontaneously resolved HCV; chronic, untreated HCV; cured HCV (HCV RNA-negative); or HCV treatment failures (HCV RNA-positive). Poisson regression was used to compare incidence rates between HCV groups for end-stage liver disease (ESLD; including hepatocellular carcinoma [HCC]), non-acquired immunodeficiency virus defining malignancy (NADM; excluding HCC), and cardiovascular disease (CVD). RESULTS: There were 16 618 persons included (median follow-up 8.3 years, interquartile range 3.1-13.7). There were 887 CVD, 902 NADM, and 436 ESLD events; crude incidence rates/1000 person-years follow-up were 6.4 (95% confidence interval [CI] 6.0-6.9) for CVD, 6.5 (95% CI 6.1-6.9) for NADM, and 3.1 (95% CI 2.8-3.4) for ESLD. After adjustment, there were no differences in incidence rates of NADM or CVD across the 5 groups. HCV-negative individuals (adjusted incidence rate ratio [aIRR] 0.22, 95% CI 0.14-0.34) and those with spontaneous clearance (aIRR 0.61, 95% CI 0.36-1.02) had reduced rates of ESLD compared to cured individuals. Persons with chronic, untreated HCV infections (aIRR 1.47, 95% CI 1.02-2.13) or treatment failure (aIRR 1.80, 95% CI 1.22-2.66) had significantly raised rates of ESLD, compared to those who were cured. CONCLUSIONS: Incidences of NADM or CVD were independent of HCV group, whereas those cured had substantially lower incidences of ESLD, underlining the importance of successful HCV treatment for reducing ESLD.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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".