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Record W4393187957 · doi:10.3390/medicina60040536

Atrial Fibrillation Burden: Impact on Stroke Risk and Beyond

2024· article· en· W4393187957 on OpenAlexaff
Ahmed AlTurki, Vidal Essebag

Bibliographic record

VenueMedicina · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsHôpital du Sacré-Cœur de MontréalMcGill University Health Centre
FundersBoston Scientific Corporation
KeywordsAtrial fibrillationStroke (engine)CardiologyMedicineStroke riskInternal medicineIschemic strokeIschemia

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is an important independent risk factor for stroke. Current guidelines handle AF as a binary entity with risk driven by the presence of clinical risk factors, which guides the decision to treat with an oral anticoagulant. Recent studies in the literature suggest a dose-response relationship between AF burden and stroke risk, in both clinical AF and subclinical atrial fibrillation (SCAF), which differs from current guidance to disregard burden and utilize clinical risk scores alone. Within clinical classification and at the same risk levels in various scores, the risk of stroke increases with AF burden. This opens the possibility of incorporating burden into risk profiles, which has already shown promise. Long-term rhythm monitoring is needed to elucidate SCAF in patients with stroke. Recent data from randomized trials are controversial regarding whether there is an independent risk from AF episodes with a duration of less than 24 h, including the duration of SCAF greater than six minutes but less than 24 h.

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)0.002
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.340
Teacher spread0.314 · 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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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