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Record W2892682517 · doi:10.1111/jpc.14226

Putting our best foot forward: Clinical, treatment‐based and ethical considerations of nusinersen therapy in Canada for spinal muscular atrophy

2018· review· en· W2892682517 on OpenAlexaffabout
Sonya Vukovic, Laura McAdam, Randi Zlotnik Shaul, Reshma Amin

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2018
Typereview
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsSickKids FoundationHolland Bloorview Kids Rehabilitation HospitalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSpinal muscular atrophyMedicineSMA*Life expectancyPediatricsOrphan drugClinical trialDiseasePhysical therapyIntensive care medicinePhysical medicine and rehabilitationInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Spinal muscular atrophy (SMA) is the most common genetic cause of infant mortality. SMA is a spectral disorder and is categorised based on symptom onset and severity. The median life expectancy for infants with SMA presenting before 6 months of age is less than 2 years without respiratory support. To date, there is no cure for SMA. In June 2017, nusinersen was approved in Canada as the first disease-modifying drug for SMA because of its demonstrated benefits on motor function and survival in clinical trials. However, with a price tag of almost 1 million dollars for the first year of therapy, careful clinical, treatment-based and ethical consideration of the principles of (i) best interests; (ii) universality; (iii) portability; (iv) public administration; (v) accessibility; and (vi) comprehensiveness are important guideposts to ensure transparent and equitable allocation of health-care resources for nusinersen and all other future orphan drugs.

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 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)0.006
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.837
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.144
GPT teacher head0.462
Teacher spread0.318 · 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
GenreReview

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

Citations11
Published2018
Admission routes2
Has abstractyes

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