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Record W2284880360 · doi:10.1586/14779072.2016.1154457

Asymptomatic atrial fibrillation burden and thromboembolic events: piecing evidence together

2016· review· en· W2284880360 on OpenAlexaff
Riccardo Proietti, Christopher Labos, Ahmed AlTurki, Vidal Essebag, Taya V. Glotzer, Atul Verma

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2016
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsSouthlake Regional Health CenterMcGill University Health Centre
Fundersnot available
KeywordsMedicineAtrial fibrillationAsymptomaticStroke (engine)CardiologyInternal medicineThromboembolic strokeIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Contributory evidence on a direct association between asymptomatic atrial fibrillation (AF) burden and thromboembolic events is conflicting and contradictory. The aim of the article is to gather evidence available for a direct correlation between burden and stroke. METHODS: A literature search was performed to capture studies reporting data on the impact of asymptomatic AF burden on the risk of stroke. Data was then extracted from each included study including burden of AF, hazard ratio (HR) for stroke, and CHADS2 score. A random effects meta-analysis was carried out on the log-transformed HRs for different subgroups of AF burden. A meta-regression was performed on the two variables: burden of asymptomatic AF and CHADS2 score. RESULTS: The random-effect pooled analysis performed on a single subgroup of the six studies reporting data on HR, showed a HR of 2.150 (95% CI 1.523-3.003) for stroke during asymptomatic AF compared to sinus rhythm. At univariate meta-regression, no correlation was detected between burden of asymptomatic AF and HR for stroke (p-value 0,874). When CHADS2 score was included in the regression model as a covariate, no significant association was detected (p-value 0,939). CONCLUSION: A direct correlation between burden of asymptomatic AF and HR for stroke cannot be detected in our pooled analysis. However, due to the limitations acknowledged in the analysis, our findings need to be confirmed in large cohort studies.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.007
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 head0.094
GPT teacher head0.403
Teacher spread0.309 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations9
Published2016
Admission routes1
Has abstractyes

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