P.072 Alberta Spinal Muscular Atrophy Newborn Screening (SMA-NBS) – 2022 results
Bibliographic record
Abstract
Background: Spinal muscular atrophy (SMA) is a progressive neuromuscular disease caused by biallelic mutations of the survival motor neuron 1 (SMN1) gene. Early diagnosis via newborn screening and presymptomatic treatment are essential to optimize health outcomes for affected individuals. Methods: We developed a multiplex real-time polymerase chain reaction assay using dried blood spot samples for the detection of homozygous deletion of exon 7 of the SMN1 gene. Newborns who were screened positive were seen urgently for clinical evaluations. Copy numbers of SMN1 and SMN2 genes were determined by multiplex ligation-dependent probe amplication for confirmatory testing. Results: From February 28, 2022 to December 31, 2022, 42,450 newborns were screened in Alberta. Four infants had abnormal screen results and were subsequently confirmed to have SMA. No false positive newborns were detected. Three infants received adeno-associated virus serotype 9 (AAV9)-mediated SMN1 gene replacement therapy <31 days of age. One infant received SMN2-splicing modulator treatment due to maternally-transferred AAV9 neutralizing antibodies prior to gene therapy at 3 months of age. Conclusions: The estimated incidence of SMA in Alberta is 9.4 (95% CI: 2.5 – 24.1) per 100,000 live-births. During the first year of the SMA-NBS program, 4 asymptomatic infants received treatment and demonstrated excellent developmental progress to date.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".