The International Rare Diseases Research Consortium: Policies and Guidelines to maximize impact
Bibliographic record
Abstract
The International Rare Diseases Research Consortium (IRDiRC) has agreed on IRDiRC Policies and Guidelines, following extensive deliberations and discussions in 2012 and 2013, as a first step towards improving coordination of research efforts worldwide. The 25 funding members and 3 patient umbrella organizations (as of early 2013) of IRDiRC, a consortium of research funders that focuses on improving diagnosis and therapy for rare disease patients, agreed in Dublin, Ireland in April 2013 on the Policies and Guidelines that emphasize collaboration in rare disease research, the involvement of patients and their representatives in all relevant aspects of research, as well as the sharing of data and resources. The Policies and Guidelines provide guidance on ontologies, diagnostics, biomarkers, patient registries, biobanks, natural history, therapeutics, models, publication, intellectual property, and communication. Most IRDiRC members-currently nearly 50 strong-have since incorporated its policies in their funding calls and some have chosen to exceed the requirements laid out, for instance in relation to data sharing. The IRDiRC Policies and Guidelines are the first, detailed agreement of major public and private funding organizations worldwide to govern rare disease research, and may serve as a template for other areas of international research collaboration. While it is too early to assess their full impact on research productivity and patient benefit, the IRDiRC Policies and Guidelines have already contributed significantly to improving transparency and collaboration in rare disease research.
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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 | 0.294 | 0.378 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.037 | 0.020 |
| Open science | 0.016 | 0.023 |
| Research integrity | 0.052 | 0.032 |
| Insufficient payload (model declined to judge) | 0.022 | 0.031 |
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".