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Record W4404330049 · doi:10.1017/cjn.2024.319

Equitable Access to Disease-Modifying Therapies for Canadian Children with SMA and Four <i>SMN2</i> Copies

2024· article· en· W4404330049 on OpenAlexaffvenueabout
Hugh J. McMillan, Hernán Gonorazky, Craig Campbell, Nicolas Chrestian, Megan Crone, James J. Dowling, Kristina Joyal, Hanna Kolski, Edward Leung, Alex MacKenzie, Jean K. Mah, Laura McAdam, Elisa Nigro, Cam‐Tu Émilie Nguyen, Maryam Oskoui, Chantal Poulin, Jordan Sheriko, Mark A. Tarnopolsky, Jiri Vajsar, Amanda Yaworski, Kathryn Selby

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2024
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of British ColumbiaMcMaster Children's HospitalMcGill UniversityUniversity of OttawaMcGill University Health CentreDalhousie UniversityCentre Hospitalier Universitaire Sainte-JustineAlberta Children's HospitalStollery Children's HospitalHealth Sciences CentreGlenrose Rehabilitation HospitalUniversity of CalgaryChildren's Hospital of Western OntarioUniversity of TorontoBC Children's HospitalHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsSMA*DiseaseMedicineComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1310.005

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 head0.067
GPT teacher head0.330
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes3
Has abstractyes

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