Serum Matrix Metalloproteinase‐9 and Cognitive Impairment After Acute Ischemic Stroke
Bibliographic record
Abstract
Background The impact of serum matrix metalloproteinases‐9 (MMP‐9) on cognitive impairment after ischemic stroke is unclear. We aimed to investigate the association between serum MMP‐9 in the short‐term acute phase of ischemic stroke and cognitive impairment at 3 months. Methods and Results Our study was based on a subsample from the CATIS (China Antihypertensive Trial in Acute Ischemic Stroke); a total of 558 patients with serum MMP‐9 levels from 7 of 26 participating sites of the trial were included in this analysis. Cognitive impairment severity was categorized as severe, mild, or none (Mini‐Mental State Examination score, <23, 23–26, or ≥27, respectively; Montreal Cognitive Assessment score, <20, 20–24, or ≥25, respectively). Cognitive impairment was defined as a score of <27 for Mini‐Mental State Examination or <25 for Montreal Cognitive Assessment. According to Mini‐Mental State Examination score, 143 participants (25.6%) had mild cognitive impairment and 153 (27.4%) had severe cognitive impairment at 3 months. After adjustment for age, National Institutes of Health stroke score, education, and other covariates, the odds ratio for the highest quartile of serum MMP‐9 compared with the lowest quartile was 3.20 (95% confidence interval, 1.87–5.49) for cognitive impairment. Multiple‐adjusted spline regression model showed a linear association between MMP‐9 levels and cognitive impairment ( P <0.001 for linearity). Sensitivity and subgroup analyses further confirmed these results. Similar significant findings were observed when cognitive impairment was defined by Montreal Cognitive Assessment score. Conclusions Increased serum MMP‐9 levels in the short‐term phase of ischemic stroke were associated with 3‐month cognitive impairment, independently of established risk factors.
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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".