A.3 The Canadian registry for amyloidosis research: a national multi-disciplinary registry for real-world evidence
Bibliographic record
Abstract
Background: The Canadian Registry for Amyloidosis Research (CRAR) is a nationwide disease registry of transthyretin (ATTR) and light-chain (AL) amyloidosis. Recent advances in disease-modifying therapy have improved prognosis, however there is a critical need for real-world evidence to address knowledge gaps, particularly longer-term therapeutic outcomes and surveillance strategies. Methods: A multi-stakeholder process was undertaken to develop a consensus dataset for ATTR- and AL-amyloidosis. This process included surveys to rank the importance of potential data items, and a consensus meeting of the CRAR steering committee, (comprised of multidisciplinary clinical experts, and patient organization representatives). Patients and patient organizations supported the development and implementation of a patient-reported dataset. Results: Consensus data items include disease onset, progression, severity, treatments, and outcomes, as well as patient-reported outcomes. Both prospective and retrospective (including deceased) patient cohorts are included. Further baseline data will be presented on an initial cohort of patients. Conclusions: CRAR has been established to collect a longitudinal, multidisciplinary dataset that will evaluate amyloidosis care and outcomes. CRAR has launched at multiple specialty amyloidosis centers nationally and is continually expanding. The growth of this program will promote opportunities to assess real-world safety and efficacy and inform the cost-effectiveness of therapies while supporting patient recruitment for 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 | 0.050 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".