Randomised clinical trial: pre‐dosing with taribavirin before starting pegylated interferon vs. standard combination regimen in treatment‐naïve patients with chronic hepatitis C genotype 1
Bibliographic record
Abstract
BACKGROUND: Combination therapy with the ribavirin (RBV) prodrug taribavirin (TBV) and pegylated interferon (PIFN) has produced lower rates of anaemia than with RBV and PIFN. Studies have demonstrated that the sharpest decline in viral load during TBV therapy occurs at Weeks 4 through 6, when TBV reaches steady-state blood levels. AIM: The current proof-of-concept study was conducted to examine whether first-order viral kinetics could be influenced by pre-dosing TBV to steady state before introducing PIFN. METHODS: Therapy-naïve patients with chronic hepatitis C virus (HCV) genotype 1 (G1) were randomised to receive (i) TBV 600 mg BID monotherapy for 4 weeks followed by combination therapy with PIFN [pre-dosing arm (n = 23)] or (ii) TBV administered concurrently with PIFN [standard dosing arm (n = 19)]. RESULTS: More patients achieved undetectable virus or a ≥2-log(10) reduction of HCV RNA at Week 4 in the pre-dosing vs. the standard dosing arm [33% vs. 22% (P = 0.497)]. There was also a trend towards greater reduction in mean log(10) change in HCV RNA in the pre-dosing vs. the standard dosing arm, which was statistically significant at Day 1 [-0.34 ± 0.46 vs. 0.09 ± 0.32 (P < 0.003)] but not at other time points up to Week 24. No significant difference was observed in the rates of anaemia (haemoglobin <10 g/dL) between study arms (4.5% vs. 5.3%). CONCLUSIONS: Pre-dosing TBV prior to starting PIFN produces a trend towards improved efficacy although statistical significance was not reached in this small patient population. These results warrant larger clinical trials of TBV pre-dosing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".