Factors Influencing Post-Stroke Cognitive Impairment in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Objective: Type 2 diabetes mellitus (T2DM) and ischemic stroke, which are common diseases among older people, are closely related to cognitive impairment. This study aims to investigate the influencing factors of post-stroke cognitive impairment (PSCI) in patients with T2DM. Methods: We enrolled 161 patients with T2DM who experienced acute ischemic stroke and were hospitalized in the Department of Neurology, Jinan Central Hospital, Shandong, China. Cognitive function was evaluated with the Montreal Cognitive Assessment scale. According to the results, patients were divided into three groups-the cognitively normal group, mild cognitive impairment group, and severe cognitive impairment group. We analyzed general demographic data, laboratory information, imaging data, the results of neuropsychological evaluation, and clinical features as well as influencing factors of PSCI in these patients and established a prediction model. Results: The three groups of patients were significantly different in terms of age, education level, course of diabetes mellitus (DM), recurrent cerebral infarction (RCI), and other factors. RCI, course of DM, and glycated hemoglobin (HbA1c) were independent risk factors of PSCI in patients with T2DM, with odds ratio (95% confidence interval): 7.17 (2.09, 30.37), 5.39 (2.40, 14.59), and 3.89 (1.66, 10.04), respectively, whereas female, senior high school, serum albumin were protective factors: 0.28 (0.07, 0.95), 0.05 (0.01, 0.21), 0.20 (0.08, 0.42), respectively. Furthermore, we constructed a prediction model using sex, age, education level, RCI, DM course, HbA1c and serum albumin and obtained a receiver operating characteristic (ROC) curve. The area under the ROC curve is 0.966, suggesting the significant association of these influencing factors with PSCI in patients with T2DM. Conclusion: In this study, the occurrence of PSCI in patients with T2DM was related to RCI, course of DM, and HbA1c, among other factors. Attention to influencing factors is needed in these patients for early diagnosis and timely intervention of cognitive impairment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".