The association between serum uric acid and cognitive performance in patients with ischemic stroke is modified by estimated glomerular filtration rate
Bibliographic record
Abstract
The relationship between serum uric acid (SUA) and poor cognitive performance in patients with ischemic stroke is unclear. We hypothesized that the severity of renal function mediates the association between SUA and cognitive dysfunction.A retrospective analysis of 608 patients with ischemic stroke was conducted between 2016 and 2020. SUA was obtained from inpatient medical records. Global cognitive function via mini-mental state exam (MMSE) and Montreal Cognitive Assessment (MoCA) was determined one month after hospital discharge. The relationship between SUA and cognitive function was assessed by multiple linear and logistic regression analyses. Patients had a mean age of 66.6 years (SD: 4.1 years), and 52% were male. The mean SUA level was 298.6 ± 75.4 μmol/L. SUA increases were significantly positively associated with lower MMSE and MoCA scores and increased risk of moderate-severe cognitive impairment one month after stroke (p < 0.01), even after adjusting for factors including age, gender, BMI, diabetes and hypertension history. Adding a term for estimated glomerular filtration rate (eGFR) attenuated these associations such that SUA was no longer associated with cognitive performance. A fully adjusted stronger negative association between SUA and cognitive performance was found in those who had lower eGFR, with a significant eGFR interaction for MMSE (p-interaction = 0.016) and MoCA (p-interaction = 0.005). In patients with ischemic stroke, SUA showed an inverse association with cognitive function among those who have lower eGFR. The renal function might mediate the association between SUA and cognitive dysfunction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".