Hepatitis C Direct Acting Antivirals and Ribavirin Modify Lipid but not Glucose Parameters
Bibliographic record
Abstract
Chronic hepatitis C (HCV) infection perturbs lipid and glucose metabolism. The influenceof direct acting antiviral (DAA) treatment and ribavirin on these measures was evaluated.Furthermore, the effect of HCV cure on these parameters was assessed. Participants were allocatedto one of three 12-week treatment groups: non-cirrhotic genotype 1aparitaprevir/ritonavir/ombitasvir/dasabuvir (PrOD) plus ribavirin; non-cirrhotic 1b-PrOD;compensated cirrhotic 1a or 1b-PrOD plus ribavirin. Fasting insulin, glucose, lipid andapolipoprotein measures were assessed at baseline, Treatment Weeks 4 and 12, and 12 and 24 weekspost-dosing. Twenty-three of 24 participants achieved SVR (PP= 23/24, 96% SVR). Overall, totalcholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride levels all increased intreatment and post-dosing. However, LDL-C levels decreased during treatment in ribavirinrecipients. Fasting glucose, insulin, and HOMA-IR were unchanged during treatment and 12 weekspost-treatment. By 12 weeks post-treatment, controlled attenuation parameter (CAP) scores, ameasure of steatosis, increased from baseline (mean 30.3 ± 63.5, p = 0.05). This regimen was safe andhighly effective and did not influence glucose metabolism. Ribavirin exposure may mitigate someon-treatment lipid changes. Further mechanistic studies are needed to understand how ribavirinimpacts lipid pathways, as there could be therapeutic implications. The metabolic pathophysiologyof increased CAP score with HCV treatment requires explanation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".