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Record W2888906623 · doi:10.1093/eurheartj/ehy565.1358

1358Prevalence of silent vascular brain lesions among patients with atrial fibrillation and no known history of stroke

2018· article· en· W2888906623 on OpenAlexaff
Steffen Blum, M Kuehne, Nicolas Rodondi, A Mueller, Peter Ammann, Giorgio Moschovitis, Richard Kobza, J. Schlaepfer, Pascal Meyre, Leo H. Bonati, Georg Ehret, Christian Sticherling, Matthias Schwenkglenks, Stefan Osswald, David Conen

Bibliographic record

VenueEuropean Heart Journal · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)CardiologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Patients with atrial fibrillation (AF) have an increased risk of stroke. However, the total burden of ischemic or hemorrhagic brain lesions in AF patients remains largely unknown. We assessed the prevalence of such lesions on cerebral magnetic resonance imaging (cMRI) in a large and unselected cohort of AF patients. Methods: Swiss-AF is a prospective multicenter observational AF cohort study in Switzerland (n=2,415; 13 sites). cMRI was performed in all eligible patients based on a standardized protocol. All scans were reviewed in a central core lab according to standardized criteria. Results: cMRI scans were available in 1,736 patients. 230 (13%) patients had a previous history of stroke (95% ischemic and 5% hemorrhagic) and 159 (9%) a history of a transient ischemic attack (TIA). Among the 1,388 patients without a history of stroke or TIA, 366 (26%) were women and the mean age was 72±9 years. Of these, 1,234 (89%) were on OAC and the mean CHA2DS2-VASc Score was 2.8±1.4. Two hundred and seven (15%) patients had silent ischemic infarctions, 222 (16%) lacunes and 269 (19%) microbleeds. Only 819 (59%) patients had no evidence for silent vascular brain lesions.

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)0.000
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 score0.031
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.249
Teacher spread0.216 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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