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Record W2884962542 · doi:10.1017/cjn.2018.59

Response to the Canadian Agency for Drugs and Technologies in Health and Institut national d’excellence en santé et en services sociaux decision regarding nusinersen for Spinal Muscular Atrophy

2018· article· en· W2884962542 on OpenAlexaffvenueabout
Craig Campbell, Kathryn Selby, Hugh J. McMillan, Jiri Vajsar, Lawrence Korngut, Bernard Brais, Alex MacKenzie, Maryam Oskoui

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2018
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsAgricultural Research Institute of OntarioHospital for Sick ChildrenUniversity of OttawaMcGill UniversityUniversity of CalgaryChildren's Hospital of Western OntarioChildren's Hospital of Eastern OntarioMontreal Neurological Institute and HospitalSickKids FoundationWestern UniversityUniversity of TorontoBC Children's HospitalLondon Health Sciences Centre
Fundersnot available
KeywordsExcellenceAgency (philosophy)Spinal muscular atrophyMedicinePolitical scienceFamily medicineSociologyInternal medicineDiseaseLaw

Abstract

fetched live from OpenAlex

Response to the Canadian Agency for Drugs and Technologies in Health and Institut national d’excellence en santé et en services sociaux decision regarding nusinersen for Spinal Muscular Atrophy - Volume 45 Issue 5

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)0.015
metaresearch head score (Gemma)0.072
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.949
Threshold uncertainty score0.729

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.072
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0030.003
Science and technology studies0.0130.006
Scholarly communication0.0080.003
Open science0.0080.004
Research integrity0.1170.055
Insufficient payload (model declined to judge)0.0360.016

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 head0.040
GPT teacher head0.363
Teacher spread0.323 · 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
GenreCommentary

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

Citations1
Published2018
Admission routes3
Has abstractyes

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