Evidence and values: requirements for public reimbursement of drugs for rare diseases--a case study in oncology.
Bibliographic record
Abstract
INTRODUCTION: Doubts have been expressed about whether standard methods of health technology assessment are suitable for the evaluation of drugs for rare diseases. Under conditions of rarity, it may be more difficult to conduct large randomized trials in order to gather adequate evidence on efficacy, and the standard methods of economic evaluation may not adequately reflect societal preferences for the treatment of serious and/or life-threatening rare diseases. METHODS: A roundtable was held at the University of Toronto Joint Centre for Bioethics on February 18, 2008 to address these issues. While the focus was on evaluation and reimbursement decision-making for rare cancers, the discussion was broadened to consider the place of evidence and values in considering public reimbursement of drugs prescribed for rare disorders more generally. DISCUSSION: This paper explores the relevant issues in more detail, using the example of a new drug for treatment of renal cell carcinoma. CONCLUSION: There should be a greater commitment by reimbursement agencies to a fair and transparent decision-making process with appropriate community input. Criteria should be developed to validate surrogate markers for rare diseases. It should also be acknowledged that the traditional measures of benefit in economic studies do not incorporate all elements of social value. The need should be recognized to balance equity with an efficient use of resources.
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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 | 0.141 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.025 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".