The IRDiRC Chrysalis Task Force: making rare disease research attractive to companies
Bibliographic record
Abstract
Background: The International Rare Diseases Research Consortium (IRDiRC) is an international initiative that aims to use research to facilitate rapid diagnosis and treatment of rare diseases. Objective: IRDiRC launched the Chrysalis Task Force to identify key financial and nonfinancial factors that make rare disease research and development attractive to companies. Methods: The Chrysalis Task Force was comprised of thought leaders from companies, patient advocacy groups, regulatory agencies, and research funders. The Task Force created a survey that was distributed to companies of different sizes with varied investment portfolios and interests in rare disease research. Based on the survey results, the Task Force then conducted targeted interviews. Results: The survey and interview respondents identified several factors that make rare disease research and development attractive (e.g. a good understanding of the underlying biology) as well as barriers (e.g. absence of an advocacy organization representing the affected community's needs). The concept of Return On Investment allowed the exploration of factors that were weighed differently by survey and interview respondents, depending on a number of intrinsic and extrinsic issues. Conclusions: The Chrysalis Task Force identified factors attributable to rare disease research and development that may be of interest to and actionable by funders, academic researchers, patients and their families, companies, regulators, and payers in the medium term to short term. By addressing the identified challenges, involved parties may seek solutions to significantly advance the research and development of treatments for rare diseases.
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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 | 0.184 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".