Global partnerships in rare disease research
Bibliographic record
Abstract
Rare diseases collectively impact hundreds of millions worldwide, yet the genetic causes of many remain unknown or poorly understood. Model organisms (MOs) - such as yeast, fly, zebrafish and mouse - provide powerful experimental systems for functional validation of candidate genes and variants, elucidation of gene function and disease mechanisms, and identification of potential therapeutic targets and treatments. However, gaps persist between clinical gene discovery and MO-based research. The Canadian Rare Diseases: Models and Mechanisms (RDMM) Network was established in 2014 to address this gap by linking clinicians with MO researchers through a scientist registry and peer-reviewed funding process. Over the past decade, the RDMM Network has funded over 160 collaborative projects, enabled insights into numerous rare conditions, and led to sustained partnerships and external funding. The RDMM Registry software has been adopted internationally, forming a network of interoperable registries that enable cross-border collaborations and expand access to MO expertise worldwide. Going forward, the Canadian RDMM Network remains committed to sharing its tools, processes and experience to help establish new RDMM-like networks worldwide and invites the global research community to join efforts to accelerate rare disease research.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".