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The Canadian Community Utilization of Stroke Prevention Study in Atrial Fibrillation in the Emergency Department (C-CUSP ED)

2018· article· en· W2898234809 on OpenAlexafffundabout
Ratika Parkash, Kirk Magee, Mark McMullen, Michael Clory, Michel D’Astous, Martin Robichaud, Gary Andolfatto, Brandi Read, Jia Wang, Lehana Thabane, Clare Atzema, Paul Dorian, Janusz Kaczorowski, Davina Banner, Robby Nieuwlaat, Noah Ivers, Thao Huynh, Janet Curran, Ian D. Graham, Stuart J. Connolly, Jeff S. Healey

Bibliographic record

VenueAnnals of Emergency Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of OttawaMcGill University Health CentreUniversity of TorontoUniversity of Northern British ColumbiaSt. Michael's HospitalSunnybrook Health Science CentreNova Scotia Health AuthorityImpactDr. Georges-L.-Dumont University Hospital CentreMcMaster UniversityLions Gate HospitalUniversité de MontréalPopulation Health Research InstituteQueen Elizabeth II Health Sciences Centre
FundersUniversité de MontréalUniversity of TorontoMcGill University Health CentreUniversity of OttawaMcGill UniversityBoehringer IngelheimHeart and Stroke Foundation of CanadaBayerMcMaster UniversityUniversity of Northern British ColumbiaPfizer
KeywordsMedicineAtrial fibrillationEmergency departmentMedical prescriptionOdds ratioStroke (engine)Confidence intervalEmergency medicineRetrospective cohort studyInternal medicinePediatrics

Abstract

fetched live from OpenAlex

STUDY OBJECTIVE: Lack of oral anticoagulation prescription in the emergency department (ED) has been identified as a care gap in atrial fibrillation patients. This study seeks to determine whether the use of a tool kit for emergency physicians with a follow-up community-based atrial fibrillation clinic resulted in greater oral anticoagulation prescription at ED discharge than usual care. METHODS: This was a before-after study in 5 Canadian EDs in 3 cities. Patients who presented to the ED with atrial fibrillation were eligible for inclusion. The before phase (1) was retrospective; 2 after phases (2 and 3) were prospective: phase 2 used an oral anticoagulation prescription tool for emergency physicians and patient education materials, whereas phase 3 used the same prescription tool, patient materials, atrial fibrillation educational session, and follow-up in an atrial fibrillation clinic. Each phase was 1 year long. The primary outcome was the rate of new oral anticoagulation prescription at ED discharge for patients who were oral anticoagulation eligible and not receiving oral anticoagulation at presentation. RESULTS: score greater than or equal to 1. The rate of new oral anticoagulation prescription in phase 1 was 15.8% compared with 54.1% and 47.2%, in phases 2 and 3, respectively. After multivariable adjustment, the odds ratio for new oral anticoagulation prescription was 8.03 (95% confidence interval 3.52 to 18.29) for phase 3 versus 1. The 6-month rate of oral anticoagulation use was numerically but not significantly higher in phase 3 compared with phase 2 (71.6% versus 79.4%; adjusted odds ratio 2.30; 95% confidence interval 0.89 to 5.96). The rate of major bleeding at 6 months was 0%, 0.8%, and 1% in phases 1, 2, and 3, respectively. CONCLUSION: An oral anticoagulation prescription tool was associated with an increase in new oral anticoagulation prescription in the ED, irrespective of whether an atrial fibrillation clinic follow-up was scheduled. The use of an atrial fibrillation clinic was associated with a trend to a higher rate of oral anticoagulation at 6-month follow-up.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.847
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.327
GPT teacher head0.474
Teacher spread0.146 · 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".

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Citations17
Published2018
Admission routes3
Has abstractyes

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