The CNDR: Collaborating to Translate New Therapies for Canadians
Bibliographic record
Abstract
BACKGROUND: Patient registries represent an important method of organizing "real world" patient information for clinical and research purposes. Registries can facilitate clinical trial planning and recruitment and are particularly useful in this regard for uncommon and rare diseases. Neuromuscular diseases (NMDs) are individually rare but in aggregate have a significant prevalence. In Canada, information on NMDs is lacking. Barriers to performing Canadian multicentre NMD research exist which can be overcome by a comprehensive and collaborative NMD registry. METHODS: We describe the objectives, design, feasibility and initial recruitment results for the Canadian Neuromuscular Disease Registry (CNDR). RESULTS: The CNDR is a clinic-based registry which launched nationally in June 2011, incorporates paediatric and adult neuromuscular clinics in British Columbia, Alberta, Ontario, Quebec, New Brunswick and Nova Scotia and, as of December 2012, has recruited 1161 patients from 12 provinces and territories. Complete medical datasets have been captured on 460 "index disease" patients. Another 618 "non-index" patients have been recruited with capture of physician-confirmed diagnosis and contact information. We have demonstrated the feasibility of blended clinic and central office-based recruitment. "Index disease" patients recruited at the time of writing include 253 with Duchenne and Becker muscular dystrophy, 161 with myotonic dystrophy, and 71 with ALS. CONCLUSIONS: The CNDR is a new nationwide registry of patients with NMDs that represents an important advance in Canadian neuromuscular disease research capacity. It provides an innovative platform for organizing patient information to facilitate clinical research and to expedite translation of recent laboratory findings into human studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 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".