The Association Between Serum Uric Acid Levels and the Risk of Cognitive Dysfunction in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Patients with atrial fibrillation (AF) are linked to an increased risk of cognitive dysfunction, and serum uric acid levels play an important factor in cognitive dysfunction. However, the optimal serum uric acid level in patients with AF remains unclear. Therefore, we aimed to explore the relationship between serum uric acid and cognitive dysfunction. 583 patients were conducted in the Affiliated Hospital of Jining Medical University. Cognitive dysfunction was assessed by the Montreal Cognitive Assessment (MoCA). The relationship between serum uric acid levels and the risk of cognitive dysfunction in patients with AF was analyzed using the smoothing spline fitting model and threshold analysis. The average serum uric acid level was (383.26 ± 110.11) μmol/L, and the incidence of cognitive dysfunction was 79.76%. There was a non‐linear relationship between serum uric acid levels and the risk of cognitive dysfunction in patients with AF, and the inflection point was 352 μmol/L. At the left of the inflection point, the relationship was significant (OR = 1.02, 95% CI = 1.00–1.04). At the right of the inflection point, there was no statistical difference ( p = 0.101). When serum uric acid levels are less than 352 μmol/L, the risk of cognitive dysfunction increases by 2% for each unit increase in serum uric acid levels in patients with AF. The study provides evidence for the treatment of serum uric acid levels in patients with AF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".