Relationship Among Homocysteine, Inflammation and Cognitive Impairment in Patients with Acute Ischemic Stroke and Transient Ischemic Attack
Bibliographic record
Abstract
PURPOSE: To investigate the associations among homocysteine (Hcy), inflammation and cognitive impairment in patients with acute ischemic stroke (AIS) and transient ischemic attack (TIA). PATIENTS AND METHODS: Patients included were enrolled from a subgroup of China National Stroke Registry-III (CNSR-III). We used a Chinese version of Montreal Cognitive Assessment (MoCA) to screen for cognitive impairment. We used high-sensitivity C-reactive protein (hsCRP) level to reflect the inflammatory status, which was assessed at baseline together with Hcy concentration. The primary outcome was the incidence of post-stroke cognitive impairment (PSCI) at 3 months after AIS and TIA. Multivariable logistic regression analysis was used to evaluate the correlation between Hcy and hsCRP, and their effects on cognition. RESULTS: We enrolled 1466 patients with a median age of 62 (54-70) years old, including 895 (61.05%) patients with elevated Hcy levels, 466 (31.79%) with increased hsCRP concentrations, and 755 (51.50%) with PSCI. In the group of patients with hyperhomocysteinemia (HHcy), higher hsCRP levels were related to cognitive impairment, whether or not adjusted for multiple potential confounders (crude OR: 1.71,95% CI: 1.29-2.27, p < 0.01; adjusted OR: 1.42, 95% CI: 1.04-1.93, p = 0.03). No significant interactions for the impact on PSCI were observed in subgroups stratified by age, sex or Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification (P interaction > 0.05 for all). CONCLUSION: High inflammatory levels increase the risk of cognitive impairment in HHcy patients after AIS and TIA.
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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) | 0.001 | 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 | 0.001 | 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".