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Record W2972440611 · doi:10.5414/cn109724

Cardiologists’ and nephrologists’ management of atrial fibrillation in hemodialysis patients

2019· article· en· W2972440611 on OpenAlexaboutno aff
Laura Quinn Marcus, Linda MacKeigan, Kori Leblanc, David Orlov, Nicholas Mitsakakis, Zubin Austin, Sarbjit V. Jassal, Marisa Battistella

Bibliographic record

VenueClinical Nephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationAntithromboticStroke (engine)HemodialysisManagement of atrial fibrillationInternal medicineNephrologyIntensive care medicinePopulationCardiologyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Antithrombotic therapy for stroke prevention in atrial fibrillation (AF) is considered a standard of care, but for hemodialysis (HD) patients the benefits are unclear, and bleeding risks are high. Our study objective was to compare cardiologists' and nephrologists' stroke prevention practices in different patient risk scenarios. MATERIALS AND METHODS: A cross-sectional, online survey was distributed to members of three Canadian physician societies (Nephrology, Cardiovascular, Heart Rhythm), and to cardiologists affiliated with three Canadian Universities. The questionnaire included four AF scenarios in HD patients with varying stroke and bleeding risks. Physicians selected one of six antithrombotic therapy options for each scenario. RESULTS: Cardiologists were 3 times more likely than nephro-logists to choose anticoagulant therapy over both antiplatelet and no drug therapy, regardless of stroke or bleeding risk (p < 0.001). Physicians' drug therapy choices in regards to level of stroke and bleeding risk reflected the expected pattern based on current evidence. CONCLUSION: Cardiologists were more likely to prescribe anticoagulant therapy for AF in the HD population compared to nephrologists, regardless of patient stroke or bleeding risk.

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 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 score0.445

Codex and Gemma teacher scores by category

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

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 head0.058
GPT teacher head0.366
Teacher spread0.308 · 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.

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
Published2019
Admission routes1
Has abstractyes

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