Toward Value-Based Pricing to Boost Cancer Research and Innovation
Bibliographic record
Abstract
The high market price of new anticancer agents has stimulated debate about the long-term sustainability of healthcare systems and whether these new agents can continue to be supported by public healthcare or by private insurers. In addition, some drugs have been approved with limited clinical benefit, raising concerns about setting a minimum requirement for medical benefit. Options to resolve these problems include raising the bar for approval of new drugs and/or pricing of new agents based on the medical benefit that they offer to patients. In this commentary, we suggest that new agents should be marketed in a two-step process that would include first the approval of the new drug by the regulatory agencies and second the introduction of a market price based on the medical benefit that the new intervention offers to patients. Introduction of value-based pricing would maintain the sustainability of health care systems and would improve drug development, as it would pressure pharmaceutical companies to become more innovative and avoid the development of compounds with limited benefit. Value-based pricing could also stimulate the funding of research directed to development of new anticancer drugs with novel mechanisms of action. Cancer Res; 76(11); 3127-9. ©2016 AACR.
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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 | 0.028 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".