P1894Serum light-chain neurofilament is associated with brain atrophy in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Background and aims There is emerging evidence that atrial fibrillation (AF) is associated with cognitive dysfunction, increased risk for dementia and reduced brain volume independent of stroke, but the underlying mechanisms of these associations remain unclear. Here, we investigated the association of serum light-chain neurofilament (sNfL), a neuroaxonal injury biomarker, with brain atrophy in AF patients. Methods Explorative analysis from the Swiss-AF cohort study, a multicenter prospective observationalstudy which recruited patients aged ≥45 years with documented AF (NCT02105844). In baseline blood samples, sNfL concentrations were measured in duplicates using a single-molecule array assay. Brain MRI was obtained at baseline and at two years using a standardized protocol including a 3D T1-weighted MPRAGE sequence, on which Structural Image Evaluation using Normalization of Atrophy (SIENA) with optimized parameters for brain extraction was applied to calculate the two-year percentage whole brain volume change (PBVC). Results We included 245 Swiss-AF patients (median age 73, 73% male). Two-year PBVC was significantly associated with baseline sNfL in linear regression, with a 0.09% whole brain volume decrease per 10 pg/ml sNfL increase (95% CI [0.05–0.13], p<0.001). This association remained significant after adjustment for age, history of stroke and other vascular risk factors. Neurofilament and brain atrophy Conclusion Increasing baseline sNfL was predictive of higher two-year brain atrophy rates independent of stroke history in AF patients. This association might reflect a chronic neurodegenerative process in AF. Acknowledgement/Funding Swiss National Science Foundation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".