P4625Association between pattern of atrial fibrillation and neurocognitive function in patients at low risk of stroke
Bibliographic record
Abstract
Growing evidence suggests that the rate of cognitive impairment and dementia is magnified in patients with AF, independently of clinical stroke. The strongest association is observed in patients <75 years. However, it remains unclear whether type of AF influences cognitive function. Method: Patients with AF and a low risk of stroke randomized in the BRAIN-AF trial (NCT02387229) underwent global cognitive assessment at baseline using the MoCA score (range 0–30). A cut-off value <26 has a sensitivity of 80–100% and specificity of 50–76% for detecting mild cognitive impairment. A cut-off value <24 is less sensitive for this purpose (84%) but has greater specificity (81%). Inclusion criteria were: documented AF; CHADS2 score of 0, and age <62 years. Main exclusion criteria were: known dementia; major depression; need for anticoagulant or antiplatelet therapy and increased risk of bleeding. Results: A total of 219 patients, mean age 52.9±7.3 years, 20% female were enrolled and had permanent (N=30, 13.7%), persistent (N=28, 12.8%), or paroxysmal (N=161, 73.2%) AF (Table). All subjects had depression and global cognitive function assessments. Multivariate analyses adjusted for age and education level. A higher proportion of patients with permanent and persistent AF had a MoCA score <26 (23.3% and 21.4%, respectively) compared to patients with paroxysmal AF (8.7%; p=0.0461). Similar results were obtained with a MoCA cut-off value<24 (13.3% versus 7.1% versus 1.9%, respectively, p=0.0115). There was a trend towards a lower mean MoCA score in patients with permanent versus paroxysmal AF (p=0.0515).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".