P2908Serum light-chain neurofilament, a brain lesion marker, correlates with CHA2DS2-VASc score among patients with atrial fibrillation: a cross-sectional study
Bibliographic record
Abstract
Background: Serum light-chain neurofilament (sNfL) is an emerging biomarker for neuroaxonal injury in inflammatory, neurodegenerative and vascular brain disease. Its role as a potential neurological outcome marker in heart disease has not been investigated. Our aim was to study if sNfL is associated with the CHA2DS2-VASc score, a validated score predicting stroke risk in patients with atrial fibrillation (AF). Methods: sNfL was measured at baseline in 278 patients with AF included in the SWISS-AF cohort study (mean age 73 years, 75% male), 90% of whom were under oral anticoagulation. Linear regression was used to investigate associations between log-transformed sNfL and (1) CHA2DS2-VASc alone, (2) CHA2DS2-VASc adjusted for age, and (3) all components of the score (congestive heart failure, hypertension, age, diabetes, stroke and TIA history, vascular disease, sex) in a multivariable analysis. Findings: sNfL was significantly associated with the CHA2DS2-VASc score (figure), also after correction for age (p<0.001). On average, sNfL levels increase by 22.3% per unit increase in CHA2DS2-VASc score. In the multivariable model including the score components, age (4.7% increase per year) and diabetes (62.5% increase) were independently associated with sNfL (p<0.001 each), as was stroke history by trend (29.4% increase; p=0.06).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".