Current status of the cost burden of first-line systemic treatment for patients with advanced hepatocellular carcinoma in Japan, 2021–22
Bibliographic record
Abstract
BACKGROUND: Although recent advances in systemic therapies for hepatocellular carcinoma (HCC) have led to prolonged patient survival, the high costs of the drugs place a heavy burden on both patients and society. The objectives of this study were to examine the treatment regimens used as first-line systemic treatment for patients with advanced HCC in Japan and to estimate the treatment costs per regimen. METHODS: For this study, we aggregated the data of patients who had received first-line systemic treatment for advanced HCC between July 2021 and June 2022. The treatment cost per month of each regimen was estimated based on standard usage, assuming an average weight of 60 kg for male patients. The data were categorized by the treatment regimen, and the treatments were categorized based on the cost into very high-cost (≥1 000 000 Japanese yen [JPY]/month), high-cost (≥500 000 JPY/month) and other (<500 000 JPY/month) treatments. RESULTS: Of the total of 552 patients from 24 institutions whose data were analyzed in this study, 439 (79.5%) received atezolizumab plus bevacizumab, 98 (17.8%) received lenvatinib and 15 (2.7%) received sorafenib as the first-line treatment. The treatment cost per month for each of the above regimens was as follows: atezolizumab plus bevacizumab, 1 176 284 JPY; lenvatinib, 362 295 JPY and sorafenib, 571 644 JPY. In total, 82.2% of patients received high-cost regimens, and the majority of these patients received a very high-cost regimen of atezolizumab plus bevacizumab. CONCLUSIONS: Advances in systemic therapies for HCC have led to prolonged patient survival. However, the treatment costs are also increasing, imposing a burden on both the patients and society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".