Association between Systemic Immune Inflammation Index and Cognitive Impairment after Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Aims: Post-stroke cognitive impairment (PSCI) is one of the major complications after ischemic stroke. PSCI has been shown to be associated with low-grade systemic inflammation. As a novel inflammatory marker, the systemic immune-inflammation (SII) index could reflect clinical outcomes in severe cardiovascular diseases. We therefore performed a prospective study to investigate the correlation between the SII index and the risk of PSCI in patients with ischemic stroke. Methods: We prospectively enrolled 254 patients with ischemic stroke with symptoms onset <72 h. The SII index was detected within 24 h after admission. The Montreal Cognitive Scale (MoCA) was utilized to evaluate cognitive function, and PSCI was defined as a MoCA score of <25 points. Results: During the 3-month follow-up, 70 participants (27.6%) had mild cognitive impairment and 60 (23.6%) had severe cognitive impairment. In binary logistic regression analysis, each one-standard deviation increase in the SII index was significantly associated with the prevalence of PSCI after adjusting for age, sex, and other confounders (odds ratio 2.341; 95% confidence interval, 1.439–3.809, p = 0.001). Similar significant findings were observed when SII was defined as a categorical variable. In addition, the multiple-adjusted spline regression model showed a linear association between the SII index and cognitive impairment (p = 0.003 for linearity). Conclusions: Our study indicated that an increased SII index was closely related to PSCI at 3 months in patients with ischemic stroke. Further research is required to evaluate the efficacy of inflammation management in these patients.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".