Real‐world medical costs of antiviral therapy among patients with chronic HCV infection and advanced hepatic fibrosis
Bibliographic record
Abstract
Abstract Background and Aims Very potent direct acting antivirals for the treatment of chronic hepatitis C virus infection were recently introduced into daily clinical practice. Currently, treatment uptake is hampered by their high costs, eliciting prioritization of treatment. We aimed to evaluate the direct medical costs during interferon (IFN)‐based antiviral treatment and the costs per sustained virological response (SVR) among patients with advanced hepatic fibrosis. Methods This retrospective cohort study included all consecutive patients with chronic hepatitis C virus infection and biopsy‐proven bridging fibrosis or cirrhosis (Ishak 4–6) treated with IFN‐based regimens in five hepatology units of tertiary care centers in Europe and Canada. Direct medical costs, expressed in 2013 Euros, during therapy were assessed. The components of care were quantified by three distinct categories: treatment, safety/ monitoring, and complications. Cost per SVR was calculated by dividing the mean cost by the SVR rate. Results In total, 672 interferon‐based treatments administered to 455 patients were included. Total medical costs per patient were averaged to €14 559 (95% confidence interval [CI], €13 323–€15 836). The mean cost per SVR was €38 514 (95% CI, €35 244–€41 892). The costs per SVR were €26 105 (95% CI, €23 068–€29 296) for patients with a normal platelet count and €50 907 (95% CI, €44 151–€59 612) for patients with thrombocytopenia, with the costs per SVR of €74 961 (95% CI, €55 463–€103 541) among those patients with a platelet count below 100 * 109/L. Conclusions Because of the lower SVR rates, the cost per SVR of IFN‐based treatment increased when patients with more advanced liver disease were treated. Additional costs of IFN‐free therapy could be limited among these patients.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".