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Record W3012643609 · doi:10.1503/cmaj.191599

Canadian Stroke Best Practice Recommendations, seventh edition: acetylsalicylic acid for prevention of vascular events

2020· article· en· W3012643609 on OpenAlexaffvenueabout
Theodore Wein, M. Patrice Lindsay, David J. Gladstone, Alexandre Y. Poppe, Alan Bell, Leanne K. Casaubon, Norine Foley, Shelagh B. Coutts, Jafna L. Cox, James D. Douketis, Thalia S. Field, Laura Gioia, Jeffrey Habert, Eddy Lang, Shamir R. Mehta, Christine Papoushek, William Semchuk, Jacob A. Udell, Stephanie Lawrence, Anita Mountain, Gord Gubitz, Dar Dowlatshahi, Anne Simard, Andrea de Jong, Eric E. Smith

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsHome and Community Care Support ServicesUniversity of SaskatchewanUniversity Health NetworkHealth Sciences CentreWomen's College HospitalUniversity of CalgaryUniversity of British ColumbiaMcMaster UniversityOntario Stroke NetworkToronto General HospitalUniversity of TorontoDalhousie UniversityHôpital Notre-DameHeart and Stroke FoundationSunnybrook Health Science CentreMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsStroke (engine)Cognitive impairmentMedicineFamily medicineEmergency medicineCognitionPsychiatryEngineering

Abstract

fetched live from OpenAlex

KEY POINTS In 2016, 270 204 people in Canada (excluding Quebec) were admitted to hospital for heart conditions, stroke and vascular cognitive impairment, including 107 391 women and 162 813 men, of whom 91 524 died.[1][1] This equates to 1 out of every 3 deaths in Canada and outpaces other diseases

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)0.009
metaresearch head score (Gemma)0.031
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 score0.979
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0060.006
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0070.002
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0100.007

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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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

Citations21
Published2020
Admission routes3
Has abstractyes

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Same venueCanadian Medical Association JournalSame topicAntiplatelet Therapy and Cardiovascular DiseasesFrench-language works237,207