Prognostic Value of Glucose-to-Potassium Ratio in Acute Ischemic Stroke Patients Undergoing Thrombolysis and Its Interaction with Inflammation
Bibliographic record
Abstract
Lingling Lin,1,&ast; Rui Zhang,2,3,&ast; Jianing Wang,4,&ast; Yichuan Fan,2,5 Wei Xie,2,6 Bohuai Yu,2,6 Jialing Lou,2,6 Yanyi Pan,2,6 Chao Chen,7 Suwen Huang,2 Guangyong Chen,1 Yiyun Weng2 1Department of Neurology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 2Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 3Renji College, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 4Department of Neurology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 5Alberta Institute, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 6The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 7Department of Nutriology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guangyong Chen, Department of Neurology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China, Email gychen@wmu.edu.cn Yiyun Weng, Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China, Email wengyiyun2012@126.comObjective: The glucose-to-potassium ratio (GPR) has been proven to be an early predictor of central nervous system injury. Meanwhile, it has a potential interaction with the inflammatory response. Therefore, we aimed to comprehensively analyze the prognostic value of GPR for thrombolytic acute ischemic stroke (AIS) patients and its synergistic effect with the neutrophil-to-lymphocyte ratio (NLR).Methods: AIS patients treated with thrombolysis were retrospectively enrolled at the First Affiliated Hospital of Wenzhou Medical University between February 1st, 2018, and December 31st, 2021. Cox and Logistic regression were used for evaluating the predictive value of GPR for the prognosis of AIS patients. Patients were grouped according to GPR and NLR levels to study the synergistic effect of GPR and NLR.Results: In a cohort of 606 patients, after adjusting for significant confounding factors in a multivariate regression analysis, GPR was able to independently predict adverse outcomes such as 6-mRS [odds ratio (OR) = 1.743, 95% confidence interval (CI): 1.271– 2.389, p = 0.001]. The synergistic analysis of GPR and NLR showed that for 6-mRS, GPR-H/NLR-L (OR = 2.888, 95% CI: 1.213– 6.874, p = 0.017), GPR-H/NLR-M (OR = 2.757, 95% CI: 1.179– 6.447, p = 0.019) and GPR-H/NLR-H (OR = 5.195, 95% CI: 2.320– 11.634, p < 0.001) were significantly associated with adverse outcomes.Conclusion: GPR independently predicts adverse outcomes in AIS patients, and its addition to the prediction model improves predictive accuracy. There’s a synergistic effect between GPR and NLR on adverse outcomes.Keywords: glucose-to-potassium ratio, neutrophil-to-lymphocyte ratio, acute ischemic stroke
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".