Peripheral immunity is associated with cognitive impairment after acute minor ischemic stroke and transient ischemic attack
Bibliographic record
Abstract
Immunoinflammation is associated with the development of post-stroke cognitive impairment (PSCI), however, peripheral immunity has not been fully explored. We aimed to investigate the association between PSCI and peripheral immune indicators, including neutrophil, lymphocyte, and mononuclear percentages and counts; the systemic immune inflammation index; platelet-to-lymphocyte ratio; neutrophil-to-lymphocyte ratio (NLR); and lymphocyte-to-monocyte ratio. A total of 224 patients with acute minor ischemic stroke or transient ischemic attack with 6-12 months of follow-up were included. PSCI was defined as a Montreal Cognitive Assessment score < 22 during the follow-up period. We performed logistic regression, subgroup analyses based on age and sex, and further established predictive models. We found that increased innate immunity indicators (neutrophils, neutrophil percentage) increased the risk of PSCI, whereas increased adaptive immunity indicator (lymphocytes) were protective against PSCI, especially in patients aged 50-65 years. Neutrophil percentage and NLR improved the predictive efficacy of the models that included demographic, clinical, and imaging information, with the area under the curve increased from 0.765 to 0.804 and 0.803 (P = 0.042 and 0.049, respectively). We conducted a comprehensive analysis of peripheral immunity in PSCI, providing a novel perspective on the early detection, etiology, and treatment of PSCI.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".