Translating rare-disease therapies into improved care for patients and families: what are the right outcomes, designs, and engagement approaches in health-systems research?
Bibliographic record
Abstract
There is a need for research to understand and improve health systems for rare diseases in order to ensure that new, efficacious therapies developed through basic and early translational science lead to real benefits for patients. Such research must (i) focus on appropriate patient-oriented outcomes, (ii) include robust study designs that can accommodate real-world decision priorities, and (iii) involve effective stakeholder-engagement strategies. For transformative therapies, study outcomes will need to shift toward longer-term goals in recognition of success in preventing catastrophic outcomes. For incremental therapies, outcomes should be selected in recognition of the impact of care on quality of life for patients and families. To generate new evidence, we suggest that hybrid study designs integrating elements of practice-based observational research and pragmatic trials hold the most promise for addressing priorities such as minimizing bias, accounting for cointerventions, identifying long-term impacts, and considering clinical heterogeneity. To effectively engage with stakeholders, a knowledge exchange infrastructure is needed to foster collaboration among patients with rare diseases and their families, health-care providers, researchers, and policy decision makers. A key priority for these groups must be collaboration toward a shared understanding of the outcomes that are of most relevance to the facilitation of patient-centered care.
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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 | 0.064 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".