Predicting efficacy and safety outcomes in patients with hepatitis C virus genotype 1 and persistently ‘normal’ alanine aminotransferase levels treated with peginterferon α‐2a (40KD) plus ribavirin
Bibliographic record
Abstract
BACKGROUND: Currently, the approved dosage of ribavirin has not been studied in patients with 'normal' alanine aminotransferase (ALT) levels. METHODS: Modelling and simulations were performed using generalised additive models (GAMs) to predict the incidence of anaemia and rate of sustained virological response (SVR) in patients with hepatitis C virus (HCV) genotype 1 and persistently 'normal' ALT levels treated with peginterferon alpha-2a (40KD) 180 microg/week plus ribavirin 1000/1200 mg/day for 48 weeks. RESULTS: Model-based simulations predicted that SVR rates would increase from 39 to 48% if patients with genotype 1 and persistently 'normal' ALT levels had received the standard weight-adjusted dose of ribavirin. This was similar to the predicted 49% SVR rate for genotype 1 patients with elevated ALT levels. The incidence of anaemia was predicted to increase from 13% to 23% in patients with persistently 'normal' ALT activity and was higher than that predicted for patients with elevated ALT levels; however, the difference appeared to be largely explained by the higher proportion of women in the former group. CONCLUSIONS: Simulations based on GAM suggest that regimens for patients with HCV genotype 1 should include the standard weight-adjusted dose of ribavirin, as similar SVR rates are predicted to be achieved, regardless of patients' ALT status at baseline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".