Direct acting antiviral HCV treatment does not influence renal function
Bibliographic record
Abstract
HCV infection is associated with chronic kidney disease due to several mechanisms. Patients treated with interferon-based regimens demonstrate improved renal function and reduced incidence of chronic kidney disease. There is scarce evidence on the effect of direct acting antiviral regimens (DAAs) on renal function.We evaluated serial measures of renal function in a cohort of HCV-infected participants following completion of DAA-based treatment regimens.Measures of glomerular filtration rate (GFR) were estimated by the CKD-EPI equation. Data was recorded at end of treatment, and at 6-12 months, 12-24 months, and greater than 24 months following treatment completion. Group-based trajectory modeling was used to determine distinct GFR trajectories. Predictors of group membership were determined by multinomial regression analysis.Six trajectories were identified. One trajectory comprising 27% of the cohort demonstrated declining renal function and the others demonstrated no change in renal function over time. Baseline GFR did not predict SVR. Diabetes was associated with lower post-treatment GFR but patients with diabetes did not demonstrate a decrease in GFR over the period of evaluation. Cirrhosis and SVR were not significant predictors of GFR or GFR trajectory.There is no clinically relevant change in renal function among the majority of HCV-infected patients following completion of DAA-based treatments. Renal function does not influence the efficacy of DAA-based regimens. No consistent effect of DAA treatment and/or SVR on renal function was observed over a 2-year period following treatment completion.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".