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Record W2563309972 · doi:10.1111/anec.12417

Anticoagulation in patients at high risk of stroke without documented atrial fibrillation. Time for a paradigm shift?

2016· article· en· W2563309972 on OpenAlexaff
Antoni Bayés de Luna, Adrián Baranchuk, Manuel Martínez‐Sellés, Pyotr G. Platonov

Bibliographic record

VenueAnnals of Noninvasive Electrocardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Internal medicineCardiologySubclinical infectionRisk factorEmbolismIschemic strokeIschemia

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is currently considered a risk factor for stroke. Depending on the severity of clinical factors (risk scores) a recommendation for full anticoagulation is made. Although AF is most certainly a risk factor for ischemic stroke, it is not necessarily the direct cause of it. The causality of association between AF and ischemic stroke is questioned by the reported lack of temporal relation between stroke events and AF paroxysms (or atrial high-rate episodes detected by devices). In different studies, only 2% of patients had subclinical AF > 6 minutes in duration at the time of stroke or systemic embolism. Is it time to consider AF only one more factor of endothelial disarray rather than the main contributor to stroke? In this "opinion paper" we propose to consider not only clinical variables predicting AF/stroke but also electrocardiographic markers of atrial fibrosis, as we postulate this as a strong indicator of risk of AF/stroke. We ask if it is time to change the paradigm and to consider, in some special situations, to protect patients (preventing stroke) who have no evidence of AF.

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 score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.318
Teacher spread0.285 · 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

Citations30
Published2016
Admission routes1
Has abstractyes

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