Bibliographic record
Abstract
What Was the Question? There is a lack of centralized information about rare disease registries (RDRs) in Canada. To address this, we created an inventory of RDRs in Canada and international RDRs that include patients living in Canada. What Did We Do? Information about RDRs was identified through a search of published literature, grey literature, consultations with members of the rare disease community, and a survey with the registry holders. What Did We Find? We identified 148 RDRs, of which 66 are RDRs in Canada and 82 international RDRs. In total, 21% of RDRs in Canada and 11% of international RDRs capture rare cancer(s). About a half of RDRs in Canada (53%) and international RDRs (46%) collaborate with other national or international registries or networks. Most RDRs in Canada (73%) and international RDRs (86%) identify patients for RDR enrollment through health care provider referrals. Electronic medical records (68%), clinician-reported data (68%), and medical chart abstraction (60%) are the most common sources of data for RDRs in Canada compared to international RDRs, for which patient (79%) and caregiver (61%) surveys are most common. Most RDRs in Canada collect clinical data (95%), laboratory and diagnostic data (85%), health outcomes data (83%), and treatment data (78%), and less commonly patient-generated data (55%) and caregiver data (15%). What Does This Mean? The information in the inventory will help guide future initiatives for improving the RDR landscape in Canada. Most RDRs in Canada source data from electronic health records, clinician-reported data, and medical charts. Fewer RDRs in Canada implement patient and caregiver surveys. RDRs in Canada include information relevant to decision-makers as most collect clinical data, health outcomes data, and treatment data, and about half collect health resource utilization data. This inventory will support future initiatives to assess the suitability of RDRs for generating decision-grade real-world evidence.
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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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.053 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".