Interleukin-6 as Predictor of One-Year Cognitive Function After Ischemic Stroke or TIA
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The relationship between inflammatory markers and cognitive decline in a poststroke setting is still unclear. We aimed to investigate the association between interleukin-6 (IL-6) and cognitive decline after acute ischemic stroke and transient ischemic attack (TIA). METHODS: In this prespecified prospective substudy of the Impairment of CognitiON and Sleep after acute ischemic stroke or transient ischemic attack in Chinese patients (ICONS) study, a total of 1003 patients with baseline IL-6 levels and completed standard 3-month and 1-year cognitive function evaluation were included. Cognitive decline was defined according to a reduction of Montreal Cognitive Assessment (MoCA) ≥2 between 3 months and one year. Multivariable logistic regression analysis was used to determine the association. RESULTS: Totally, 238 (23.73%) patients had post-stroke cognitive decline at one year. IL-6 levels were classified into four groups according to their quartile. Patients in the highest quartile of IL-6 level had higher risk of cognitive decline than those in the first quartile (25.90% vs 16.80%, adjusted OR, 1.95; 95% CI, 1.13-3.38, P = 0.0167), after adjusting for potential risk factors. CONCLUSION: Elevated IL-6 levels were independently associated with reduction of Montreal Cognitive Assessment after ischemic stroke 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".