The Need for Transparency and Efficiency in Reimbursement Decisions Relating to Drugs for Rare Diseases
Bibliographic record
Abstract
Johnson and others, 1 in their response to our recent paper, 2 highlight the need for transparency with respect to economic evaluations within rare diseases. We agree and wish to stress our belief that the same concern must be attached to reimbursement decisions with respect to such diseases. To demonstrate our commitment to such transparency, we would like to take this opportunity to respond to the specific concerns raised by Johnson and others. In addressing the external validity of our model, Johnson and others compare their interpolation of our results to the results of extension studies relating to the use of eculizumab, 3,4 Open-label extension studies in which patients are followed beyond the randomized phase of a clinical trial can be unreliable in assessing the efficacy of therapies primarily due to thelackofacomparatorgroupandpotentialselection bias due to dropouts. 5 At best, such studies may provide useful information relating to safety. The study by Hillmen and others 3 illustrates the concern over dropouts. The number of patients at onset, 195, had declined to 26 patients by 3 years and further to 9 patients by 5 years: the two time frames cited by
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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 | 0.056 | 0.236 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.063 | 0.064 |
| Insufficient payload (model declined to judge) | 0.004 | 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".