Atrial Fibrillation in Patients with Neuropathological Diagnosis of Primary Alzheimer’s Disease (I9-5A)
Bibliographic record
Abstract
OBJECTIVE: To characterize the clinical, neuropsychological and neuropathological profile of Alzheimer’s disease (AD) patients with and without atrial fibrillation(AF). BACKGROUND: The prevalence of dementia and AF increase with age. Recent evidence shows that AF patients are at a higher risk of developing cognitive impairment and dementia, irrespective of their stroke history. Thus, AF constitutes a promising target for the prevention of dementia. DESIGN/METHODS: We compared demographic data, vascular risk factors, functional status (clinical dementia rating), and neuropsychological functioning of neuropathologically confirmed cases of AD from the National Alzheimer's Coordinating Center database with (AF-AD) and without (nAF-AD) AF diagnosis. We also used neural network analyses to identify neuropathology findings related with AF. RESULTS: We included 1686 patients with pathological diagnosis of AD. Patients with AF-AD were older at the time of onset of cognitive decline (77.1±9.2 vs. 71.5±11.2 years, p<0.001) and at death (85.6.5±7.7 vs. 79.8±10.4, p<0.001), and were more likely to be hypertensive (66.3 vs. 56.1[percnt], p=0.002) than those with nAF-AD. Comorbidities were also more frequent in AF-AD patients compared to nAF-AD: coronary artery disease (27.3 vs. 26.6[percnt], p<0.001), congestive heart failure (21.5 vs. 6.0[percnt], p<0.001), stroke (19.2 vs. 11.7[percnt], p<0.001), and transient ischemic attack (15.9 vs. 10.7[percnt], p=0.014). Accordingly, large infarcts (14.4 vs. 8.5[percnt], p=0.002) and the overall ischemic vascular component (large infarcts, lacunar infarcts, or microinfarcts) were more frequently among AF-AD patients than nAF-subjects (37.5 vs. 30.6[percnt], p=0.023). AF-AD patients had better performances than nAF-AD subjects in cognitive functioning (general cognitive functioning, executive function, memory, language, attention) and functional status. In the neural network analysis, AF was only related with vascular brain lesions, showing no association with neurodegenerative changes. CONCLUSIONS: AF-AD has a more "vascular" and “benign” profile than nAF-AD. Dementia seems to be mediated by brain infarcts rather than by neurodegeneration. FUNDING:National Institute on Aging (UO1 AG016976).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".