Budget impact analysis of boceprevir and telaprevir for the treatment of hepatitis C genotype 1 infection
Bibliographic record
Abstract
BACKGROUND: Boceprevir and telaprevir have recently showed dramatically better treatment outcomes than conventional PEGylated interferon plus ribavirin for the treatment of hepatitis C virus genotype 1, but the average cost per patient is unknown. METHODS: In the UK context, we performed a budget impact analysis to estimate the average per patient cost of adding boceprevir or telaprevir to PEGylated interferon plus ribavirin therapy. We considered both standard-duration therapy and response-guided therapy regimens of boceprevir and telaprevir for treatment-naïve and treatment-experienced patients. Our model utilized monthly discontinuation rates. We built a Bayesian Markov model to account for uncertainty associated with the clinical input and cost data. RESULTS: The total average cost of response-guided therapy with boceprevir is £22,850 and £25,060 for treatment-naïve and treatment-experienced patients, respectively. By comparison, the total average cost of response-guided therapy with telaprevir was £29,930 and £31,880 for treatment-naïve and treatment-experienced patients, respectively, whereas the total average cost of standard-duration boceprevir is £34,680 and £34,350 and for telaprevir was £32,530 and £31,680 for treatment-naïve and treatment experienced patients, respectively. CONCLUSION: Our results demonstrate that response-guided therapy with boceprevir is notably less costly than response-guided therapy with telaprevir. Our results also suggest that the standard treatment duration of boceprevir is slightly more costly than the standard treatment duration of telaprevir.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".