MétaCan
Menu
Back to cohort
Record W4207044513 · doi:10.1016/j.cjco.2022.01.005

Which Recommendations Are You Using? A Survey of Emergency Physician Management of Paroxysmal Atrial Fibrillation

2022· article· en· W4207044513 on OpenAlexaffabout
Ryan Daniel, Clare Atzema, Dennis D. Cho, Philip J. Davis, Lorne L. Costello

Bibliographic record

VenueCJC Open · 2022
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of SaskatchewanUniversity of TorontoUniversity Health NetworkHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsCardioversionMedicineAtrial fibrillationChecklistMedical emergencyFamily medicineStroke (engine)Emergency medicinePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Both the Canadian Cardiovascular Society (CCS) and the Canadian Association of Emergency Physicians (CAEP) have published documents to guide atrial fibrillation (AF) management. In 2021, the CAEP updated its AF checklist. Prior to this update, the recommendations of the 2 organizations differed in several key areas, including the suggested cardioversion timeframe, the factors determining cardioversion eligibility, and anticoagulant initiation after cardioversion. Whether emergency physicians (EPs) are aware of, or adhering to, one, both, or neither of these documents is unknown. Methods: We assessed document awareness, adherence, and EP practice using a piloted questionnaire administered to EPs at 5 emergency departments in 3 provinces. Results: -VASc score as their stroke-risk assessment tool. Conclusions: Although most surveyed EPs were aware of at least one organization's AF documents, many reported using parts of both. When recommendations conflicted, EPs were divided in their decision-making. These findings emphasize the need to improve consensus between organizations and further improve knowledge translation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.402
Teacher spread0.244 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCJC OpenSame topicAtrial Fibrillation Management and OutcomesFrench-language works237,207