Could the Early Detection of Atrial Fibrillation Reduce the Risk of Developing Dementia?
Bibliographic record
Abstract
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".