A Canadian Adult Spinal Muscular Atrophy Outcome Measures Toolkit: Results of a National Consensus using a Modified Delphi Method
Bibliographic record
Abstract
BACKGROUND: Spinal Muscular Atrophy (SMA) is a rare disease that affects 1 in 11 000 live births. Recent developments in SMA treatments have included new disease-modifying therapies that require high quality data to inform decisions around initiation and continuation of therapy. In Canada, there are no nationally agreed upon outcome measures (OM) used in adult SMA. Standardization of OM is essential to obtain high quality data that is comparable among neuromuscular clinics. OBJECTIVE: To develop a recommended toolkit and timing of OM for assessment of adults with SMA. METHODS: A modified delphi method consisting of 2 virtual voting rounds followed by a virtual conference was utilized with a panel of expert clinicians treating adult SMA across Canada. RESULTS: A consensus-derived toolkit of 8 OM was developed across three domains of function, with an additional 3 optional measures. Optimal assessment frequency is 12 months for most patients regardless of therapeutic access, while patients in their first year of receiving disease-modifying therapy should be assessed more frequently. CONCLUSIONS: The implementation of the consensus-derived OM toolkit will improve monitoring and assessment of adult SMA patients, and enrich the quality of real-world evidence. Regular updates to the toolkit must be considered as new evidence becomes available.
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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 | 0.140 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".