Association Between Serum Uric Acid Levels and Cognitive Function in Patients with Ischemic Stroke and Transient Ischemic Attack (TIA): A 3-Month Follow-Up Study
Bibliographic record
Abstract
PURPOSE: Cognitive impairment is a common complication after stroke and transient ischemic attack (TIA). The relationship between serum uric acid (SUA) and post-stroke cognitive impairment (PSCI) is controversial. This study evaluated the association of different SUA levels in the normal range and PSCI at 3 months. PATIENTS AND METHODS: A total of 1523 patients with ischemic stroke/TIA were recruited from the Impairment of Cognition and Sleep (ICONS) subgroup of the China National Stroke Registry-3 (CNSR-3). SUA concentration was assessed at baseline. Global cognitive status was evaluated using the Montreal Cognitive Assessment (MoCA). The main clinical outcome was the incidence of PSCI assessed at 3 months after stroke/TIA. The association between SUA status and the risk of PSCI was assessed with multiple regression models adjusted for potential covariates. RESULTS: Among the 1523 patients (1391 (91.33%) stroke patients and 132 (8.67%) TIA patients), 747 (49.05%) patients had PSCI at 3 months. Compared to the reference group, there was an increased risk of PSCI in males with SUA levels in the first (OR=1.76) and fourth quartiles (OR=1.47). A U-shaped association between SUA levels and the incidence of PSCI with an inflection point of 297 mmol/L was also found in males. However, there was no association between SUA levels and PSCI in females. CONCLUSION: The association between SUA and PSCI differed between males and females. In males, both low and high SUA levels were associated with relatively higher incidences of PSCI, supporting a U-shaped association between SUA levels and PSCI.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".