High Uric Acid Level Predicts Early Neurological Deterioration in Intracerebral Hemorrhage
Bibliographic record
Abstract
Objective: Increased level of serum uric acid (UA) is often considered a risk factor for ischemic stroke. However, there are limited data on the association between UA and intracerebral hemorrhage (ICH). This study aimed to examine the connection between UA and early neurological deterioration (END) in patients with ICH. Methods: This is a prospective observational study. Patients with ICH were enrolled from January 2017 to December 2020. END was diagnosed as the Canadian Stroke Scale (CSS) score decreased ≥ 1 points between admission and 48 hours. UA was measured at admission. Multivariable logistic regression analysis was performed to explore the relationship between serum UA and END. Results: Of the 498 enrolled patients, 132 (26.5%) were developed with END. Patients with END had a significantly higher level of serum UA (332 vs 270 μmol/L, P < 0.001). Univariate logistic regression analysis indicated that patients with the highest quartile of UA level had an OR of 3.256 (95% CI: 1.849– 5.734, P < 0.001) for END compared with those with the lowest quartile of UA level. After adjusting for major confounders, the highest UA quartile remained as an independent predictor for END (OR = 2.282, 95% CI: 1.112– 4.685, P = 0.013). Conclusion: Higher serum UA level was independently associated with END in patients with ICH; therefore, intervention to lower UA level may be worth considering. Keywords: early neurological deterioration, intracerebral hemorrhage, uric acid
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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 | 0.000 |
| 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".