The association between serum uric acid levels and the risk of cognitive dysfunction in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Background: Patients with atrial fibrillation (AF) is linked to increased risk of cognitive dysfunction, and serum uric acid levels plays an important factor in cognitive dysfunction. However, optimal serum uric acid levels in patients with AF remains unclear. Therefore, we aimed to explore the relationship of serum uric acid and cognitive dysfunction in patients with AF. Methods: 583 patients were conducted in the Affiliated Hospital of Jining Medical University, Shandong Province, China from February 2020 to November 2022. Cognitive dysfunction was assessed by Montreal Cognitive Assessment (MoCA). The relationship between serum uric acid levels and the risk of cognitive dysfunction in patients with AF was analyzed using smoothing spline fitting model and threshold analysis. Results: The average serum uric acid levels of 583 patients 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 352μmol/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). Conclusion: When the serum uric acid levels is less than 352 μmol/L, the risk of cognitive dysfunction increases by 2% for each unit increases 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 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 | 0.001 |
| 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) | 0.001 | 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".