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Record W1754047618 · doi:10.1161/strokeaha.115.010454

Rapid Assessment and Treatment of Transient Ischemic Attacks and Minor Stroke in Canadian Emergency Departments

2015· editorial· en· W1754047618 on OpenAlexafffundabout
Noreen Kamal, Michael D. Hill, Dylan Blacquière, Jean-Martin Boulanger, Karl Boyle, Brian Buck, Kenneth Butcher, Marie‐Christine Camden, Leanne K. Casaubon, Robert Côté, Andrew M. Demchuk, Dar Dowlatshahi, Véronique Dubuc, Thalia S. Field, Esseddeeg Ghrooda, Laura C. Gioia, David J. Gladstone, Mayank Goyal, Gordon Gubitz, Devin Harris, Robert G. Hart, Gary Hunter, Thomas Jeerakathil, Albert Jin, Khurshid Khan, Eddy Lang, Sylvain Lanthier, M. Patrice Lindsay, Ariane Mackey, Jennifer Mandzia, Manu Mehdiratta, Jeffrey Minuk, Wieslaw Oczkowski, Céline Odier, Andrew M. Penn, Jeffrey J. Perry, Jacqueline A. Pettersen, Stephen Phillips, Alexandre Y. Poppe, Gustavo Saposnik, Daniel Selchen, Michel Shamy, Mike Sharma, Ashkan Shoamanesh, Ashfaq Shuaib, Frank L. Silver, Grant Stotts, Richard H. Swartz, Arturo Tamayo, Jeanne Teitelbaum, Steve Verreault, Theodore Wein, Samuel Yip, Shelagh B. Coutts

Bibliographic record

VenueStroke · 2015
Typeeditorial
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSaint John Regional Hospital
FundersCanadian Institutes of Health Research
KeywordsPhysics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.012
metaresearch head score (Gemma)0.052
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.981
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.052
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0060.004
Science and technology studies0.0050.003
Scholarly communication0.0090.004
Open science0.0050.002
Research integrity0.0240.024
Insufficient payload (model declined to judge)0.0070.003

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 head0.019
GPT teacher head0.311
Teacher spread0.292 · 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
GenreEditorial

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

Citations23
Published2015
Admission routes3
Has abstractno

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