Liver enzyme normalization predicts success of Hepatitis C oral direct-acting antiviral treatment
Bibliographic record
Abstract
Purpose Monitoring of hepatitis C virus (HCV) treatment response is performed by serial HCV RNA measurements; however, this may not be useful for predicting treatment success or failure with oral direct-acting antiviral agent (DAA) therapies. Liver enzyme levels, which are elevated in chronic HCV and tend to decline on therapy, may serve as a more logistically and economically feasible alternative for monitoring treatment response. Source The Ottawa Hospital Viral Hepatitis Clinic patients (n=219), receiving interferon-free oral DAA treatments, were assessed for liver enzymes and HCV RNA levels at baseline, week 4 and ≥12 weeks post-treatment. Suppression cut points used for this analysis were ALT ≥ 40U L-1 and AST ≥ 30U L-1. The primary outcome was week 12 sustained virologic response (SVR). By our analysis, all indicators had strong PPV (>90%) but limited NPV (<25%). Principal findings Along with week 4 HCV RNA, AST . 30U L-1 and AST:ALT ratio at week 4 were associated with SVR in univariate analysis with similar PPV and NPV to HCV RNA. ALT was not predictive of DAA outcome. In multivariate models, adjusting for cirrhosis and genotype, baseline AST:ALT ratio<0.9 (but none of the week 4 indicators) was significantly associated with SVR. Conclusion Our analysis suggests that enzyme levels (particularly AST and AST:ALT ratio) provide a viable alternative to HCV RNA, with robust predictive value in determining treatment success of DAA therapies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".