Establishing a Canadian Registry of Patients with Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a devastating cause of progressive weakness, respiratory failure and death. To date there is no effective therapy to meaningfully extend survival but continuously emerging targets and putative treatments are studied in clinical trials. Canadian epidemiological data on ALS is scarce and the socioeconomic impact of ALS on Canadian society is unclear. The Canadian Neuromuscular Disease Registry (CNDR) is a national clinic-based registry of patients with neuromuscular diseases with the goal of facilitating the design and execution of clinical research. METHODS: We conducted a national stakeholder survey to assess interest for a Canadian ALS registry and an assessment of expected case ascertainment. A dataset derivation meeting was held to establish the registry medical dataset. RESULTS: We report the results of the national stakeholder survey, case ascertainment assessment, and the derived dataset that have resulted in the current implementation of a Canadian registry of patients with ALS. CONCLUSIONS: The development of this long sought-after resource is a significant step forward for the Canadian ALS patient and research communities that will result in more efficient clinical trial recruitment and advancements in our understanding of ALS 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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".