Elevated serum uric acid is associated with cognitive impairment in acute minor ischemic stroke patients
Bibliographic record
Abstract
Background: Acute minor ischemic stroke (AMIS) has been proven to be strongly associated with post-stroke cognitive impairment (PSCI). Few studies have reported that uric acid (UA) levels are linked to PSCI in patients with AMIS, and those results are debatable. We investigated the relationship between serum UA levels and cognitive impairment in patients with AMIS. Methods: A total of 318 patients who were diagnosed with AMIS were recruited from Suining Central Hospital. Fasting serum samples were collected the day after admission for UA measurement. Cognitive function was evaluated at admission and 3 months after stroke using the Montreal Cognitive Assessment (MoCA). The relationship between UA and PSCI was examined using a multivariate binary logistic regression model. The optimal cut-off point for UA levels to predict PSCI was determined using the receiver operating characteristic (ROC) curve. Results: < 0.0001). The ROC curve revealed a cut-off of 363.58 μmol/L serum UA, and the predicted sensitivity and specificity for PSCI were 67.5 % and 83.5 %, respectively. Subgroup analysis showed that confounding factors had no impact on the association between serum UA and PSCI risk. Conclusions: Higher baseline serum UA levels might be an independent risk factor for cognitive impairment in AMIS patients. Serum UA levels above 363.58 μmol/L may have clinical implications in predicting PSCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".