Plasma Phenylacetylglutamine and Cognitive Impairment After Ischemic Stroke
Bibliographic record
Abstract
Background Phenylacetylglutamine was reported to be associated with ischemic stroke and cognitive performance, but its association with poststroke cognitive impairment (PSCI) remained unclear. We aimed to prospectively investigate the association between plasma phenylacetylglutamine levels and PSCI at 3 months in a multicenter cohort study. Methods A total of 617 patients with ischemic stroke were included on the basis of a preplanned ancillary study from CATIS (China Antihypertensive Trial in Acute Ischemic Stroke). PSCI was evaluated using the Mini‐Mental State Examination scale and Montreal Cognitive Assessment scale. Logistic regression analyses were performed to evaluate the association between plasma phenylacetylglutamine and the risk of 3‐month PSCI. Results According to the Mini‐Mental State Examination score, a total of 376 participants developed PSCI at 3 months. After adjustment for age, sex, education, and other important risk factors, the multivariable‐adjusted odds ratio of PSCI for the highest tertile of phenylacetylglutamine was 2.16 (95% CI, 1.32–3.54; P trend =0.002) compared with the lowest tertile. A multiple‐adjusted spline regression model showed a positive linear association of plasma phenylacetylglutamine levels with PSCI at 3 months ( P for linearity<0.001). Adding plasma phenylacetylglutamine to conventional risk factors significantly improved the risk reclassification of PSCI (net reclassification improvement: 21.35%, P =0.019; integrated discrimination index: 1.78%, P =0.003). Similar significant findings were observed when PSCI was defined by the Montreal Cognitive Assessment score. Conclusions High plasma phenylacetylglutamine levels were associated with increased odds of PSCI at 3 months, suggesting that plasma phenylacetylglutamine might be a potential predictive biomarker for PSCI among patients with ischemic stroke.
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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) | 0.000 | 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".