Future of Rare Diseases Research 2017–2027: An IRDiRC Perspective
Bibliographic record
Abstract
The International Rare Diseases Research Consortium (IRDiRC) was founded in 2011 with the conviction that rare diseases research had reached a critical juncture. Proof of principle existed that rare diseases could be diagnosed, new treatments successfully developed and approved, and improvements in quality and quantity of life achieved. Government research funders, companies, scientists, and patient advocacy groups had all demonstrated their commitment and effectiveness in contributing to progress in rare diseases research. However, the work was largely atomized, with each organization, each country, and the champions of each disease pursuing independent, often duplicative solutions. The scale of the "rare disease problem"-thousands of rare diseases, the vast preponderance of them with no approved treatment, and decades-long diagnostic odysseys for many patients-led to the realization that the time had arrived for global cooperation and collaboration among the many stakeholders active in rare diseases research, to capitalize on these proofs of principle, and maximize the output of rare diseases research efforts around the world. IRDiRC's initial aims were to aid in the achievement of two overarching objectives: to contribute to the development of 200 new therapies and the means to diagnose most rare diseases by the year 2020. 1 For more detailed information on the history, governance, and nascent stages of the Consortium, please refer to the accompanying piece on the first 6 years of IRDiRC.
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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 | 0.055 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.037 | 0.023 |
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".