Developing Recommendations to Guide Future Evidence Generation, Evidence Synthesis, and Knowledge Translation for Rare Diseases
Bibliographic record
Abstract
Introduction: The scarcity of rigorous evidence regarding rare disease therapies contributes to uncertainty for stakeholders who make decisions about the use, prescription, or funding of such therapies. My dissertation objective was to integrate stakeholder perspectives and evidence related to how rare disease therapies are evaluated to better understand drivers of uncertainty in decision making and develop an evaluation framework for future evidence generation, synthesis, and decision support. Methods: To better understand the perceived challenges in generating robust treatment effectiveness evidence, and describe various methods for mitigating these challenges, I used a meta-narrative literature review. I also conducted focus group interviews with key rare disease stakeholders (patients/caregivers, physicians, and policy advisors) to elicit different perspectives on how evidence is generated, evaluated, and synthesized in the context of health care decision making, both at a personal and population level. Finally, I integrated the focus group findings with a targeted literature review to identify characteristics of rare diseases and their candidate therapies that may warrant special consideration in health technology assessment (HTA) and health care decision making. Findings: My dissertation data revealed three fundamental challenges in generating robust treatment effectiveness evidence for rare diseases: limitations in recruiting a sufficient sample; inability to account for clinical heterogeneity; and reliance on outcomes with unclear clinical relevance. Several methodological solutions have been proposed to overcome these challenges. In addition, study participants described different perspectives on how they choose to participate in and use research in their roles as health care users, care providers, and policy advisors. Notably, conventional wisdom that patients/caregivers participate in clinical research studies because of therapeutic misconception was not supported. Finally, focus group and literature review findings identified information that potentially warrants special consideration in future HTA specific to rare diseases, including characteristics of the disease, understanding of causal hypotheses relevant to the therapy, and complexities of cost-effectiveness given the high price of many rare disease therapies. Discussion: Together, the findings from this dissertation support an evaluation framework with eight key principles that aim to mitigate important aspects of uncertainty from various stakeholder perspectives and promote evidence-informed decision making about rare disease therapies.
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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 | 0.487 | 0.824 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.037 | 0.038 |
| Open science | 0.017 | 0.022 |
| Research integrity | 0.037 | 0.033 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".