Added Therapeutic Benefits of Top‐Selling Drugs in Japan: A Cross‐Sectional Study Using Health Technology Assessment
Bibliographic record
Abstract
It is unclear whether Japanese top-selling drugs have meaningful added therapeutic benefits to justify their high sales. This question is relevant as Japan's healthcare costs are rising consistently, particularly due to increasing drug prices. This cross-sectional study evaluated the added therapeutic benefits of Japan's top-selling drugs in 2021 using ratings from established health technology assessment (HTA) agencies in Canada, France, and Germany. Drug characteristics and benefit ratings were obtained from public databases and HTA agencies, following the established method. Overall, added therapeutic benefit ratings were categorized as binary (high or low). Of 51 identified top-selling drugs in Japan, 43 (86%) had at least one rating from three agencies. Notably, 20 (47%) received low added therapeutic benefit ratings even in our optimistic scenario. Low ratings were more common among small-molecule drugs 15/20 (75%), while high ratings were predominant among biologics 14/23 (61%). Oncology drugs represented the largest category in both high 9/23 (39%) and low 5/20 (25%) groups. Interestingly, 9 drugs (9/16; 56%) approved between 2011 and 2021 received low ratings, compared to 41% (11/27) of those approved before 2011. Additionally, 70% of high-benefit drugs received at least one expedited review, whereas this was 35% for low-benefit drugs. Our findings revealed that many top-selling drugs in Japan had low added therapeutic benefits. Utilizing HTA evaluation frameworks provides valuable insights, particularly in prioritizing drugs based on added therapeutic benefits. While full implementation of such a system in Japan requires further consideration, strengthening HTA processes could help ensure sustainable healthcare costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".