Reimbursing the value of gene therapy care in an era of uncertainty
Bibliographic record
Abstract
Early-stage gene therapy (GT) clinical trials are demonstrating exciting results for persons with haemophilia (PWH), with the first products possibly licenced over the next few years for haemophilia A and B. These new treatments offer the possibility of a one-off approach to the treatment of haemophilia, with demonstrated increases in factor level expression and substantial reductions in both bleeds and factor utilization. However, clinical trial participants have demonstrated variable expression in factor levels, including decreases, over time, suggesting in some cases the effect may not last. The consequence of this uncertainty has led to challenging discussions on value and reimbursement. In most national healthcare systems, the relatively high cost of paying for GT on a one-off basis may be prohibitive, resulting in a lack of access and less post-marketing data generated, ultimately keeping these performance uncertainties high for payers. Economic models have demonstrated the cost-effectiveness of GT in haemophilia based on current clinical trial inputs, but it is in the certainty of these inputs and concomitant budget impacts where the lack of available data will be a concern for payers. To overcome the 'chicken and egg' discussion in relation to reimbursement and data, GT will necessitate new pricing and reimbursement models that share the risk between the manufacturer and the payer. New models have been described for other conditions. The aim of this paper is to propose illustrative concepts of haemophilia reimbursement models that may be further considered in the assessment of a less predictable therapeutic such as GT.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".