P.084 The Canadian Neuromuscular Disease Registry: a national spinal muscular atrophy registry for real world evidence
Bibliographic record
Abstract
Background: Patient registries are an effective tool in tracking the natural history of rare diseases as well as post-marketing surveillance of novel therapies. The Canadian Neuromuscular Disease Registry (CNDR) is a pan-neuromuscular disease registry that prospectively collects Spinal Muscular Atrophy (SMA)-specific data in 28 clinics across Canada. The objective of this study is to describe real-world data from the CNDR-SMA patient population. Methods: We report cross-sectional data from Canadian SMA patients. Patients were included in analysis if they were active (alive and with follow-up within 24 months). Results: Of 171 SMA patients included in analyses, 37% currently use non-invasive ventilation, 2% invasive ventilation, and 61% no ventilation support. Feeding tubes are used by 27% of patients. and 28% of patients have a history of scoliosis surgery. Of the 171 patients, 137 have had disease-modifying therapy: 96 on nusinersen, 22 on risdiplam, and 19 on onasemnogene abeparvovec (OA). Median (min,max) years of age at therapy initiation was 7 (0,54), 20.5 (5,53), and 1 (0,6), respectively. At therapy initiation, functional status was 32% non-sitters, 38% sitters, and 30% walkers. Conclusions: The CNDR captures a comprehensive SMA dataset that prospectively evaluates real-world data, supporting post-marketing surveillance of novel therapies in Canada.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".