A rare partnership: patient community and industry collaboration to shape the impact of real-world evidence on the rare disease ecosystem
Bibliographic record
Abstract
People with rare lysosomal storage diseases face challenges in their care that arise from disease complexity and heterogeneity, compounded by many healthcare professionals being unfamiliar with these diseases. These challenges can result in long diagnostic journeys and inadequate care. Over 30 years ago, the Rare Disease Registries for Gaucher, Fabry, Mucopolysaccharidosis type I and Pompe diseases were established to address knowledge gaps in disease natural history, clinical manifestations of disease and treatment outcomes. Evidence generated from the real-world data collected in these registries supports multiple stakeholders, including patients, healthcare providers, drug developers, researchers and regulators. To maximise the impact of real-world evidence from these registries, engagement and collaboration with the patient communities is essential. To this end, the Rare Disease Registries Patient Council was established in 2019 as a partnership between the Rare Disease Registries and global and local patient advocacy groups to share perspectives on how registry data are used and disseminated. The Patient Council has resulted in a number of patient initiatives including patient representation at Rare Disease Registries advisory boards; development of plain language summaries of registry publications to increase availability of real-world evidence to patient communities; and implementation of digital innovations such as electronic patient-reported outcomes, and patient-facing registry reports and electronic consent (in development), all to enhance patient engagement. The Patient Council is building on the foundations of industry-patient advocacy group collaboration to fully integrate patient communities in decision-making and co-create solutions for the rare disease community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".