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Record W4293173831 · doi:10.2139/ssrn.4191325

Language Discordance as a Marker of Disparities in Cerebrovascular Risk and Stroke Outcomes: A Multi-Center Prospective Canadian Study

2022· article· en· W4293173831 on OpenAlexaffabout
Ryan T. Muir, Arunima Kapoor, Megan L. Cayley, Michelle N. Sicard, Karen Lien, Alisia Southwell, Dar Dowlatshahi, Demetrios J. Sahlas, Gustavo Saposnik, Jennifer Mandzia, Leanne K. Casaubon, Ayman Hassan, Yaël Perez, Daniel Selchen, Brian J. Murray, Krista L. Lanctôt, Moira K. Kapral, Nathan Herrmann, Stephen C. Strother, Amy Yu, Peter C. Austin, Susan E. Bronskill, Richard H. Swartz

Bibliographic record

VenueSSRN Electronic Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsQueen's UniversityTrillium Health CentreSunnybrook Health Science CentreSt. Michael's HospitalLondon Health Sciences CentreMcMaster UniversityInstitute for Clinical Evaluative SciencesUniversity of OttawaToronto Western HospitalHealth Sciences CentreOttawa HospitalThunder Bay Regional Health Sciences CentreUniversity of Toronto
Fundersnot available
KeywordsCenter (category theory)Stroke (engine)MedicineStroke riskProspective cohort studyEmergency medicineIschemic strokeInternal medicineEngineering

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.007
Science and technology studies0.0090.002
Scholarly communication0.0040.001
Open science0.0040.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.367
Teacher spread0.350 · 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 designObservational
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

Citations0
Published2022
Admission routes2
Has abstractno

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