Serum Uric Acid and High-Sensitivity C-Reactive Protein as Predictors of Cognitive Impairment in Patients with Cerebral Infarction
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment induced by cerebral infarction has become a devastating health problem. More efficient predictors are required to evaluate the potential cognitive decline after cerebral infarction in clinic. Serum uric acid (UA) and high-sensitivity C-reactive protein (hs-CRP) are two factors reported to correlate with cognitive impairment. However, the understanding on serum UA and hs-CRP with cognitive dysfunction remains unclear. METHODS: Serum UA and hs-CRP were evaluated in patients with cerebral infarction (n = 197) using single factor analysis and multivariate logistic regression analysis. Clinical and pathological characteristics were analyzed by logistic regression, respectively, and the results demonstrated the correlation between the pathological characteristics and the cognitive impairment post cerebral infarction. Montreal Cognitive Assessment (MoCA) was used to evaluate the patients' cognitive function, and patients with a MoCA score <26 were recognized as with cognitive impairment. RESULTS: Clinical characteristics related to cognitive impairment, including age, gender, blood pressure, serum UA, and hs-CRP were collected and analyzed. Serum UA and hs-CRP were identified to be potential predictors for post-stroke cognitive dysfunction, with higher serum UA levels correlated with better cognitive function and higher hs-CRP levels correlated with worse cognitive impairment. CONCLUSION: Serum UA and hs-CRP are two predictors for cognitive impairment post cerebral infarction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".