Variability in Newborn Screening Across Canada: Spinal Muscular Atrophy and Beyond
Bibliographic record
Abstract
BACKGROUND: Newborn screening (NBS) identifies infants with severe, early-onset diseases, enabling early diagnosis and treatment. In Canada, decisions regarding disease inclusion in NBS programs occur at the provincial level, which leads to variability in patient care. We aimed to determine whether important differences exist in NBS programs across provinces and territories. Given that spinal muscular atrophy (SMA) is the most recent disease added to NBS programs, we hypothesized that its inclusion would show interprovincial variability and be more likely in provinces already screening for a greater number of diseases. METHODS: We conducted a cross-sectional survey of all NBS labs in Canada to understand: 1) what conditions were included in their program; 2) what genetic-based testing was performed and; 3) if SMA was included. RESULTS: = 36) and a 9-fold difference in the number of conditions screened by gene-based testing. Only nine conditions were common to all provincial NBS programs. NBS for SMA was performed in four provinces at the time of our survey, with BC recently becoming the fifth province to add SMA to their NBS on October 1, 2022. Currently, 72% of Canadian newborns are screened for SMA at birth. CONCLUSION: Although healthcare in Canada is universal, its decentralization gives rise to regional differences in NBS programs which creates inequity in the treatment, care, and potential outcomes of affected children across provincial jurisdictions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".