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Record W2052582679 · doi:10.1186/1471-2369-14-174

Warfarin use in hemodialysis patients with atrial fibrillation: decisions based on uncertainty

2013· article· en· W2052582679 on OpenAlexaffabout
S Juma, A. B. R. Thomson, Charmaine E. Lok, Catherine M. Clase, Peter G. Blake, Louise Moist

Bibliographic record

VenueBMC Nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsToronto General HospitalMcMaster UniversityUniversity of TorontoLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineWarfarinAtrial fibrillationHemodialysisRandomized controlled trialPopulationIntensive care medicineInternal medicineCardiology

Abstract

fetched live from OpenAlex

BACKGROUND: Warfarin prescribing patterns for hemodialysis patients with atrial fibrillation vary widely amongst nephrologists. This may be due to a paucity of guiding evidence, but also due to concerns of increased risks of warfarin use in this population. The literature lacks clarity on the balance of warfarin therapy between prevention of thrombotic strokes and the increased risks of bleeding in hemodialysis patients with atrial fibrillation. METHODS: We performed a survey of Canadian Nephrologists, assessing warfarin prescribing practice, and measured the certainty in making these choices. RESULTS: Respondents were consistently uncertain about warfarin use for atrial fibrillation. This uncertainty increased with a history of falls or starting hemodialysis, even when a high CHADS2 or CHA2DS2VASc score was present. The majority of respondents agreed that clinical equipoise existed about the use of oral anticoagulation in hemodialysis patients with atrial fibrillation (72.2%) and that the results of a randomized controlled trial would be relevant to their practice (98.2%). CONCLUSIONS: A randomized controlled trial of warfarin use in hemodialysis patients with atrial fibrillation would clarify the risks and benefits of warfarin use in this population.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.054
GPT teacher head0.292
Teacher spread0.238 · 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

Citations20
Published2013
Admission routes2
Has abstractyes

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