Reflections on the Canadian Bleeding Disorders Registry: Lessons Learned and Future Perspectives
Bibliographic record
Abstract
The Canadian Bleeding Disorders Registry (CBDR) has become the national registry for comprehensive care and research in hemophilia in Canada with patient, clinical, and research module connectivity. The CBDR has served as a robust resource to inform epidemiology of disease, burden of disease, and disease changes and variation over time as new treatment modalities are introduced. Information on the utilization of blood products to treat hemophilia has and can be retrieved and used by Canadian blood product procurement agencies to inform decision-making for past and future purchases. The successful multistakeholder coordination and alignment achieved over decades with the development and function of the CBDR is an exemplar that could be extended to other rare disease areas.
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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 | 0.097 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.025 | 0.034 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".