Atrial fibrillation burden and cognitive decline in elderly patients undergoing continuous monitoring
Bibliographic record
Abstract
To study the relationship between subclinical atrial fibrillation (AF) and changes in cognitive function in a large cohort of individuals with stroke risk factors. : Individuals with no prior AF diagnosis but with risk factors for stroke were recruited to undergo annual cognitive assessment with the Montreal Cognitive Assessment (MoCA) along with implantable loop recorder (ILR) monitoring for AF for 3 years. If AF episodes lasting ≥6 minutes were detected, oral anticoagulation (OAC) treatment was initiated. : A total of 1194 participants (55.2 % men, mean age 74.5 (±3.9)) had a combined duration of heart rhythm monitoring of ≈1.3 million days. Among these, 339 participants (28.3%) had adjudicated AF, with a median AF burden of 0.072% (0.02, 0.39), and 324 (96%) initiated OAC. When stratifying the participants into AF burden groups (No AF, AFlow (AF burden <0.25%), and AFhigh, (AF burden >0.25%)), only participants in the AFlow group had a decrease in MoCA score over time (P = .03), although this was not significant after adjustment for stroke risk factors. A subgroup analysis of 175 participants (14.6%) with a MoCA <26 at 3 years found no association to AF diagnosis or burden. : In a high-risk population, subclinical AF detected by continuous monitoring and subsequently treated with OAC was not associated with a significant change in MoCA score over a 3-year period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".