The Canadian Neuromuscular Disease Registry: Connecting patients to national and international research opportunities
Bibliographic record
Abstract
INTRODUCTION: Patient registries serve an important role in rare disease research, particularly for the recruitment and planning of clinical trials. The Canadian Neuromuscular Disease Registry was established with the primary objective of improving the future for neuromuscular (NM) patients through the enablement and support of research into potential treatments. METHODS: In this report, we discuss design and utilization of the Canadian Neuromuscular Disease Registry with special reference to the paediatric cohort currently enrolled in the registry. RESULTS: As of July 25, 2017, there are 658 paediatric participants enrolled in the registry, 249 are dystrophinopathies (229 are Duchenne muscular dystrophy), 57 are myotonic dystrophy participants, 98 spinal muscular atrophy participants and 65 are limb girdle muscular dystrophy. A total of 175 patients have another NM diagnosis. The registry has facilitated 20 clinical trial inquiries, 5 mail-out survey studies and 5 other studies in the paediatric population. DISCUSSION: The strengths of the registry are discussed. The registry has proven to be an invaluable tool to NM disease research and has increased Canada's visibility as a competitive location for the conduct of clinical trials for NM therapies.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".