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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".