The International Rare Diseases Research Consortium: Policies and Guidelines to maximize impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it