Atrial fibrillation diagnosed after stroke and dementia risk: cohort study of first-ever ischaemic stroke patients aged 65 or older
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF) is a risk factor for dementia among ischaemic stroke patients in whom the AF was known before the stroke (KAF). Atrial fibrillation detected after stroke (AFDAS) has a different profile compared to KAF, including less frequent cardiovascular comorbidities and lower CHA2-DS2-VASC scores. Currently, it is unknown if AFDAS is also associated with increased dementia risk. We assessed the association between AFDAS and the incident risk of dementia. We also evaluated whether the use of oral anticoagulants (OAC) was associated with lower dementia risk among AFDAS patients. METHODS AND RESULTS: In this cohort study, we classified 9791 first-ever ischaemic stroke patients from the Ontario Stroke Registry into four groups: (i) No AF, (ii) KAF, (iii) Inpatient AFDAS (diagnosed during admission), and (iv) Outpatient AFDAS (diagnosed after discharge). We used multivariable Cox proportional models to estimate hazard ratios (HR) for the association between AFDAS and incident dementia risk. Dementia was determined through administrative datasets based on previously validated algorithms. In adjusted analyses, the dementia risk was higher for inpatient AFDAS [HR 1.78, 95% confidence interval (CI) 1.51-2.10] and outpatient AFDAS (HR 1.74, 95% CI 1.47-2.05) relative to no AF. Oral anticoagulants use was associated with lower dementia risk among patients with inpatient AFDAS (HR 0.58, 95% CI 0.43-0.79) and outpatient AFDAS (HR 0.60, 95% CI 0.43-0.83). CONCLUSION: Atrial fibrillation detected after stroke was independently associated with higher risk of dementia relative to no AF. Among patients with AFDAS, the use of OACs was associated with lower dementia risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".