OP18 A Patient And Caregiver-Designed Framework For Managed Access Programs
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 programs (MAPs) are a mechanism for managing risk while enabling access to potentially beneficial drugs. Patients and their caregivers have expressed support for these programs and see patient input as critical to successful implementation. However, they have yet to be systematically involved in their design. The objective of this study was to explore what a framework for MAPs might look like when designed by patients and caregivers. 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 analyzed using a thematic network approach. Results: Patients and caregivers identified six aspects of an ideal MAP relating to accountability (program goals), governance (program-specific committee oversight; patient input; international collaboration), and evidence collection (outcome measures and stopping criteria; ongoing monitoring and registries). Additionally, patients and caregivers recognized that health care resources are finite and considered disease or drug eligibility criteria for deciding when to use a MAP (e.g. drugs treating diseases for which there are no other legitimate alternatives). Conclusions: A patient and caregiver-designed framework was created, which emphasized patient involvement and transparency. Further research is needed to examine the feasibility of this framework and roles for other stakeholders.
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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 | 0.065 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".