Managing the challenges of paying for gene therapy: strategies for market action and policy reform in the United States
Bibliographic record
Abstract
Gene therapies delivered through a single administration have revolutionized treatment possibilities for many patients living with serious or fatal conditions such as spinal muscular atrophy, hemophilia and sickle cell disease. However, shadowing the excitement about the transformational potential of many gene therapies has been widespread concern about the combination of uncertainty in the durability of their benefits over the long term and the short-term financial shock of high prices. As the healthcare payment ecosystem prepares for the growing number of gene therapies entering the market, three key interconnected challenges must be addressed: determining a fair price, managing clinical uncertainty and managing short-term budget impacts. This paper identifies specific policy reforms and market-based tools to help the US health system address these challenges to achieve more equitable and affordable access for patients to the growing number of gene therapies expected to be approved in the coming years.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.007 |
| 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".