Silent brain infarcts impact on cognitive function in atrial fibrillation
Bibliographic record
Abstract
AIMS: We aimed to investigate the association of clinically overt and silent brain lesions with cognitive function in atrial fibrillation (AF) patients. METHODS AND RESULTS: We enrolled 1227 AF patients in a prospective, multicentre cohort study (Swiss-AF). Patients underwent standardized brain magnetic resonance imaging (MRI) at baseline and after 2 years. We quantified new small non-cortical infarcts (SNCIs) and large non-cortical or cortical infarcts (LNCCIs), white matter lesions (WML), and microbleeds (Mb). Clinically, silent infarcts were defined as new SNCI/LNCCI on follow-up MRI in patients without a clinical stroke or transient ischaemic attack (TIA) during follow-up. Cognition was assessed using validated tests. The mean age was 71 years, 26.1% were females, and 89.9% were anticoagulated. Twenty-eight patients (2.3%) experienced a stroke/TIA during 2 years of follow-up. Of the 68 (5.5%) patients with ≥1 SNCI/LNCCI, 60 (88.2%) were anticoagulated at baseline and 58 (85.3%) had a silent infarct. Patients with brain infarcts had a larger decline in cognition [median (interquartile range)] changes in Cognitive Construct score [-0.12 (-0.22; -0.07)] than patients without new brain infarcts [0.07 (-0.09; 0.25)]. New WML or Mb were not associated with cognitive decline. CONCLUSION: In a contemporary cohort of AF patients, 5.5% had a new brain infarct on MRI after 2 years. The majority of these infarcts was clinically silent and occurred in anticoagulated patients. Clinically, overt and silent brain infarcts had a similar impact on cognitive decline. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02105844, https://clinicaltrials.gov/ct2/show/NCT02105844.
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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 | 0.000 |
| 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) | 0.001 | 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".