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Record W4385798118 · doi:10.1002/ajmg.a.63369

Epidemiology of spinal muscular atrophy caused by <scp><i>SMN1</i></scp> deletions in Maritime Canada

2023· article· en· W4385798118 on OpenAlexaffabout
Olivia McKee‐Muir, Sarah Dyack, Monique Taillon, Jo‐Ann Brock, Jordan Sheriko

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsCapital District Health AuthorityStan Cassidy FoundationIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsSMN1SMA*Spinal muscular atrophyIncidence (geometry)EpidemiologyMedicineDiseaseMedical geneticsPediatricsGeneticsInternal medicineBiologyGeneComputer science

Abstract

fetched live from OpenAlex

Spinal muscular atrophy (SMA), caused primarily by deletions in SMN1, leads to progressive loss of lower motor neurons. Newborn screening for SMA is under consideration for the Maritime Newborn Screening Program. The incidence of this disease has not been explored in Maritime Canada which includes the provinces of Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI). In this retrospective chart review, patients were identified from the IWK Clinical Genomics Lab and Maritime Medical Genetics Service databases for SMN1 genetic testing between 2000 and 2020. The incidence of SMA in Maritime Canada was 1:11,900. Among patients born between 2000 and 2020, NB and PEI had lower proportions of type 1 SMA (12% and 0%, respectively) when compared to NS (50%). The majority of type 1 patients had 2 copies of SMN2, the majority of type 2 patients had 3 copies, and the majority of type 3 patients had 4 copies. There was a delay to molecular diagnosis for all subtypes, longest in type 3. This study provides the best available SMA epidemiology in Maritime Canada and expands our understanding of the pattern of disease severity relative to SMN2 copy number in this region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.332
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part ASame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207