Newborn Screening for Spinal Muscular Atrophy: Ontario Testing and Follow-up Recommendations
Bibliographic record
Abstract
BACKGROUND: Spinal muscular atrophy (SMA) is characterized by the progressive loss of motor neurons causing muscle atrophy and weakness. Nusinersen, the first effective SMA therapy was approved by Health Canada in June 2017 and has been added to the provincial formulary of all but one Canadian province. Access to this effective therapy has triggered the inclusion of SMA in an increasing number of Newborn Screening (NBS) programs. However, the range of disease-modifying SMN2 gene copy numbers encountered in survival motor neuron 1 (SMN1)-null individuals means that neither screen-positive definition nor resulting treatment decisions can be determined by SMN1 genotype alone. We outline an approach to this challenge, one that specifically addresses the case of SMA newborns with four copies of SMN2. OBJECTIVES: To develop a standardized post-referral evaluation pathway for babies with a positive SMA NBS screen result. METHODS: An SMA NBS pilot trial in Ontario using first-tier MassARRAY and second-tier multi-ligand probe amplification (MLPA) was launched in January 2020. Prior to this, Ontario pediatric neuromuscular disease and NBS experts met to review the evidence regarding the diagnosis and treatment of children with SMA as it pertained to NBS. A post-referral evaluation algorithm was developed, outlining timelines for patient retrieval and management. CONCLUSIONS: Ontario's pilot NBS program has created a standardized path to facilitate early diagnosis of SMA and initiation of treatment. The goal is to provide timely access to those SMA infants in need of therapy to optimize motor function and prolong survival.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".