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Record W4401750557 · doi:10.3390/biomedicines12081931

Could the Early Detection of Atrial Fibrillation Reduce the Risk of Developing Dementia?

2024· review· en· W4401750557 on OpenAlexafffund
Fabrice Demonière, Rim Abdelli, Léna Rivard

Bibliographic record

VenueBiomedicines · 2024
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersBayer CanadaHeart and Stroke Foundation of Canada
KeywordsDementiaAtrial fibrillationMedicineStroke (engine)Cognitive declineRisk factorDiseaseConfoundingVascular dementiaIntensive care medicineCognitionCardiologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) and dementia are major global public health issues and share common risk factors, especially after the age of 65 and regardless of the presence of stroke. Despite accounting for potential confounders, AF appears to be an independent risk factor for cognitive decline and dementia. The mechanisms are likely to be multifactorial and may include AF-related ischemic stroke, cerebral hypoperfusion, microbleeds, systemic inflammation, genetic factors, and small vessel disease, leading to brain atrophy and white matter damage. The early aggressive management of AF and comorbidities may reduce the risk of dementia. Indeed, the early detection of AF-related cognitive impairment should allow for the early implementation of measures to prevent the development of dementia, mainly through integrative approaches involving the correction of risk factors and maintenance of rhythm control. Well-designed prospective studies are needed to determine whether early detection and AF treatment can prevent dementia and identify whether optimal integrative measures are effective in preventing cognitive impairment and dementia.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.114
GPT teacher head0.396
Teacher spread0.281 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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