Changes in Hepatic Steatosis Before and After Direct-Acting Antiviral Treatment in People With HIV and Hepatitis C Coinfection
Bibliographic record
Abstract
BACKGROUND: Both human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections increase the risk of hepatic steatosis (HS), which in turn contribute to the severity and progression of liver disease. Direct-acting antivirals (DAAs) can cure HCV but whether they reduce HS is unclear. METHODS: HS was assessed using the controlled attenuation parameter (CAP) and the Hepatic Steatosis Index (HSI) in participants coinfected with HIV and HCV from the Canadian Coinfection Cohort. Changes in HS, before, during, and after successful DAA treatment were estimated using generalized additive mixed models, adjusted for covariates measured prior to treatment (age, sex, duration of HCV infection, body mass index, diabetes, prior exposure to dideoxynucleosides, and hazardous drinking). RESULTS: In total, 431 participants with at least 1 measure of CAP or HSI before treatment were included. CAP steadily increased over time: adjusted annual slope 3.3 dB/m (95% credible interval [CrI], 1.6-4.9) before, and 3.9 dB/m (95% CrI, 1.9-5.9) after DAA treatment, irrespective of pretreatment CAP. In contrast, HSI changed little over time: annual slope 0.2 (95% CrI, -0.1 to 0.5) before and 0.2 (95% CrI, -0.1 to 0.5) after, but demonstrated a marked reduction during treatment -4.5 (95% CrI, -5.9 to -3.1). CONCLUSIONS: When assessed by CAP, HS was unaffected by DAA treatment and steadily increased over time. In contrast, HSI did not appear to reflect changes in HS, with the decrease during treatment likely related to resolution of hepatic inflammation. Ongoing HS may pose a risk for liver disease in coinfected people cured of HCV.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".