Systemic immune-inflammation index upon admission correlates to post-stroke cognitive impairment in patients with acute ischemic stroke
Bibliographic record
Abstract
Background: The purpose of this prospective study was to evaluate the association of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), with PSCI in patients with acute ischemic stroke (AIS). Methods: First-onset AIS patients were consecutively included from January 1, 2022 to March 1, 2023. The baseline information was collected at admission. Fasting blood was drawn the next morning. Cognitive function was assessed by the Montreal Cognitive Assessment (MoCA) 3 months after onset. Logistic regression analysis was performed to explore the correlation between SII, SIRI, and PSCI. Receiver operating characteristic (ROC) was conducted to evaluate the predictive ability of SII. Results: 332 participants were recruited, and 193 developed PSCI. Compared with patients without PSCI, the patients with PSCI had higher SII (587.75 (337.42, 988.95) vs. 345.66 (248.44, 572.89), P<0.001) and SIRI (1.59 (0.95, 2.84) vs. 1.02 (0.63, 1.55), P=0.007). SII and SIRI negatively correlated with MoCA scores (both P<0.05). The multivariable logistic regression analysis indicated that SII was independently associated with PSCI (P<0.001), while SIRI was not. The optimal cutoff for SII to predict PSCI was 676.83×109/L. Conclusions: A higher level of SII upon admission was independently correlated to PSCI three months later in AIS patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".