A Study on the Incidence and Prevalence of 5q Spinal Muscular Atrophy in Canada Using Multiple Data Sources
Bibliographic record
Abstract
OBJECTIVES: Spinal muscular atrophy (SMA) is a leading genetic cause of infant death and represents a significant burden of care. An improved understanding of the epidemiology of SMA in Canada may help inform strategies to improve the standard of care for individuals living with SMA. METHODS: We employed a multisource approach to estimate the minimal incidence and prevalence of 5q SMA and to gain greater insight into recent clinical practices and treatment trends for the Canadian SMA population. Data sources included the Canadian Paediatric Surveillance Program (CPSP), Canadian Neuromuscular Disease Registry (CNDR), and molecular genetics laboratories in Canada. RESULTS: The estimated annual minimum incidence of 5q SMA was 4.38, 3.44, and 7.99 cases per 100,000 live births in 2020 and 2021, based on CPSP, CNDR, and molecular genetics laboratories data, respectively, representing approximately 1 in 21,472 births (range 12,516-29,070) in Canada. SMA prevalence was estimated to be 0.85 per 100,000 persons aged 0-79 years. Delay in diagnosis exists across all SMA subtypes. Most common presenting symptoms were delayed milestones, hypotonia, and muscle weakness. Nusinersen was the most common disease-modifying treatment received. Most patients utilized multidisciplinary clinics for management of SMA. CONCLUSION: This study provides data on the annual minimum incidence of pediatric 5q SMA in Canada. Recent therapeutic advances and newborn screening have the potential to drastically alter the natural history of SMA. Findings underline the importance of ongoing surveillance of the epidemiology and long-term health outcomes of SMA in the Canadian population.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".