Hepatitis C antiviral treatment outcomes are comparable between clinical trial participants and recipients of standard-of-care therapy
Bibliographic record
Abstract
BACKGROUND: Trial effect refers to the impact of clinical trial participation on treatment outcomes. Little literature exists evaluating the magnitude and direction of trial effect in hepatitis C virus (HCV). METHODS: A single-center, retrospective study on HCV antiviral therapy recipients was conducted. Sustained virologic response (SVR), virologic response at treatment weeks 4 and 12, dose interruptions, and adverse events were compared between clinical trial participants and standard-of-care antiviral recipients between September 2000 and November 2011. RESULTS: A total of 449 patients were evaluated (trial: 89, nontrial: 360). Patients were matched for age (trial: 47 years, nontrial: 45 years), sex (male: trial, 74%; nontrial, 72%), and ethnicity (white: trial, 87%; nontrial, 78%). The groups differed in the incidence of genotype 1 infection (trial: 83%, nontrial 53%; P<0.001), liver biopsy rates (trial: 98%, nontrial: 66%; P<0.001), and history of psychiatric illness (trial: 30%, nontrial: 53%; P<0.001). On intent-to-treat analysis, SVR rates were found to be similar (trial: 51%, nontrial: 54%; P=0.86), even when stratified for genotype (G1: trial, 47%; nontrial, 47%; P=0.78). Interferon dose reductions (trial: 18%, nontrial: 6%; P<0.01) were more likely in trial patients, whereas treatment discontinuation because of side effects (trial: 8%, nontrial: 18%; P<0.02) was less likely in them. No differences in safety issues were identified. CONCLUSION: Overall, a trial effect resulting in improved or diminished SVR rates was not identified. Other potential positive and negative variables should be focused upon for HCV patients deliberating between clinical trial participation and receiving standard-of-care treatment.
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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 | 0.035 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".