Direct-acting antiviral hepatitis C virus treatment perturbation of the metabolic milieu
Bibliographic record
Abstract
OBJECTIVE: Hepatitis C virus (HCV), cirrhosis, and HCV medications including direct-acting antivirals (DAAs) ±ribavirin may all influence the metabolic milieu. While interferon-based regimens improve glucose tolerance, evidence is limited on DAAs. Cases of elevated lactate have recently been reported in patients treated with DAAs, and lactic acidosis is a known complication of antivirals used to treat hepatitis B virus and HIV. PATIENTS AND METHODS: Measures were evaluated at baseline, week 4, end of treatment, and 12-24 weeks after treatment. Mixed-effects modeling was used to determine factors influencing glucose and lactate over time. RESULTS: In total, 442 patients were treated (mean age 56, 65% male, 72% genotype 1, 48% cirrhotic). Glucose did not change on or after DAA treatment from baseline (P=0.51) aside from those with untreated diabetes, which declined (P=0.02). Overall, there was a decline in lactate following HCV treatment (mean 2.4-2.1 mmol/l; P<0.001). Lactate initially increased on treatment and then decreased after treatment completion in male patients treated with ribavirin. This pattern was not observed in other groups. There was no evidence of lactic acidosis with HCV nucleotide use. CONCLUSION: Distinct glucose and lactate trajectories were identified without evidence of DAA metabolic toxicity. HCV treatment does not improve random glucose levels aside from perhaps in untreated diabetic patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".