A checklist for managed access programmes for reimbursement co‐designed by Canadian patients and caregivers
Bibliographic record
Abstract
INTRODUCTION: Reimbursement decisions on orphan drugs carry significant uncertainty, and as the amount increases, so does the risk of making a wrong decision, where harms outweigh benefits. Consequently, patients often face limited access to orphan drugs. Managed access programmes (MAPs) are a mechanism for managing risk while enabling access to potentially beneficial drugs. Patients and their caregivers have expressed support for these programmes and see patient input as critical to successful implementation. However, they have yet to be systematically involved in their design. OBJECTIVE: The aim of this study was to co-design with patients and caregivers a tool for the development of managed access programmes. METHODS: Building upon established relationships with the Canadian Organization for Rare Disorders, the project team collaborated with patients and caregivers using the principles of participatory action research. Data were collected at two workshops and analysed using a thematic network approach. RESULTS: Patients and caregivers co-designed a checklist comprised of six aspects of an ideal MAP relating to accountability (programme goals); governance (MAP-specific committee oversight, patient input, international collaboration); and evidence collection (outcome measures and continuation criteria, on-going monitoring and registries). They recognized that health-care resources are finite and considered disease or drug eligibility criteria for deciding when to use a MAP (eg drugs treating diseases for which there are no other legitimate alternatives). CONCLUSIONS: A patient and caregiver-designed checklist was created, which emphasized patient involvement and transparency. Further research is needed to examine the feasibility of this checklist and roles for other stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".