Quebec Spinal Muscular Atrophy Newborn Screening Program: The First Year Experience
Bibliographic record
Abstract
Clinical trials in spinal muscular atrophy (SMA) have shown that early treatment improves outcomes, prompting inclusion in newborn screening (NBS) programs worldwide. The province of Quebec launched its SMA NBS program in October 2023, with a rapidly progressive implementation. We describe the program’s first-year experience, focusing on screening yield, birth prevalence, clinical outcomes, and challenges. In the first year, 6 of 67,933 newborns screened positive for SMA, all subsequently confirmed by diagnostic testing. Of these, 4 newborns (67%) had two SMN2 copies and 2 newborns (33%) had four copies. Additionally, one symptomatic compound heterozygote infant presented during this period, indicating a provincial birth prevalence of 1 in 9705 live births (95% CI: 1:20,032–1:4701). Two newborns with two SMN2 copies were symptomatic at initial consultation; one transitioned to palliative care and died at 43 days of life. Surviving newborns initiated treatment at a median age of 30 days (range: 9–103 days), with four receiving onasemnogene abeparvovec and one nusinersen. Motor outcomes at three or six months were stable or improved among treated infants. Overall, the Quebec SMA NBS pilot program successfully identified affected newborns, facilitated early access to therapy, and provided the first provincial estimate of SMA birth prevalence. Improved sample shipping and processing times are needed to maximize the program’s impact, which is expected with full automation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".