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Record W2621381092 · doi:10.1016/j.amjcard.2017.05.027

Use of Evidence-Based Therapy for Cardiovascular Risk Factors in Canadian Outpatients With Atrial Fibrillation

2017· article· en· W2621381092 on OpenAlexaffabout
Alexandra Silberberg, Mary Tan, Andrew T. Yan, Paul Angaran, Paul Dorian, Claudia Bucci, Jean‐Claude Grégoire, Alan Bell, David J. Gladstone, Martin S. Green, Peter L. Gross, Allan C. Skanes, Andrew M. Demchuk, Charles R. Kerr, L. Brent Mitchell, Jafna L. Cox, Mario Talajic, Vidal Essebag, Brett Heilbron, Carl Fournier, Bruce H. Wheeler, Peter Lin, Murray Berall, Anatoly Langer, Lianne Goldin, Shaun G. Goodman

Bibliographic record

VenueThe American Journal of Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsFoothills Medical CentreMcMaster UniversityHôpital Notre-DameAlberta Health ServicesUniversity of OttawaDalhousie UniversityHotchkiss Brain InstituteHealth Sciences CentreUniversity of CalgaryCanadian Heart Research CentreWestern UniversitySt. Paul's HospitalOntario Brain InstituteJuravinski HospitalQueen Elizabeth II Health Sciences CentreThrombosis and Atherosclerosis Research InstituteMcGill University Health CentreHumber River Regional HospitalUniversité de MontréalLibin Cardiovascular Institute of AlbertaSunnybrook Health Science CentreSt. Michael's HospitalUniversity of TorontoUniversity of British ColumbiaMontreal Heart Institute
Fundersnot available
KeywordsAtrial fibrillationMedicineInternal medicineCardiology

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)0.001
metaresearch head score (Gemma)0.017
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 score0.047
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.172
GPT teacher head0.352
Teacher spread0.181 · 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

Citations10
Published2017
Admission routes2
Has abstractno

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