Risk factors and clinical features of mild cognitive impairment in patients with ischemic cerebral small vessel disease： a retrospective case series study
Bibliographic record
Abstract
Objective To investigate the risk factors and clinical features of mild cognitive impairment （MCI） in patients with ischemic cerebral small vessel disease （SVD） for early diagnosis and prevention. Methods Montreal Cognitive Assessment Scale （MoCA） was used to screen MCI. The related risk factors and other clinical data were collected, and other neuropsychological tests were conducted. SVD was divided into leukoaralosis （LA）, lacunar infarction （LI）, and LA ＋ LI. Results A total of 143 patients with SVD were enrolled （68 in an MCI group, 75 in a non-MCI group）. Univariate analysis showed that there was no significant difference in the constituent ratio of age and gender between the MCI group and the non-MCI group, but the years of education in the MCI group was shorter than that in the non-MCI group, while the composition ratios of hypertension （69. 11% vs. 45. 33 % ;X2 = 8. 215, P = 0. 004）, diabetes （57. 35% vs. 40. 00% ; X2 = 4. 301, P = 0. 038 ）, hyperlipidemia （48. 53% vs. 24. 00% ; X2 = 9. 352, P = 0. 002 ）, carotid atherosclerosis （41.18% vs. 21.33% ;X2 =6. 592, P =0. 010）, and smoking （32. 35% vs. 14. 67% ;X2 =6. 285, P =0. 012）, as well as the levels of uric acid （351.81± 83.21 mmol/L vs. 323.03 ±80. 43 mmol/L; t = 2. 102, P = 0. 037） and total cholesterol （5.26± 1.26 mmol/L vs. 4. 56 ± 1.23 mmol/L; t = 3. 326, P = 0. 001） were significantly higher than those in the non-MCI group. Multivariate logistic regression analysis showed that hypertension （odds ratio OR] 2. 227, 95% confidence interval [ CI], 1. 001 -4. 954; P =0. 026）, diabetes （OR 2. 056, 95% CI 1. 862 -4. 937; P =0. 046）, hyperlipidemia （OR 2. 528, 95% CI 1. 361 - 5. 770; P =0. 028）, carotid atherosclerosis （OR 2. 658, 95% CI 1. 110 -6. 367; P =0. 029）, smoking （OR 2. 566, 95% CI 1. 017 - 6. 474; P = 0. 046）, and years of education （OR 0. 825, 95% CI 0. 745 - 0. 914; P = 0. 000） were the independent risk factors for the occurrence of MCI in patients with SVD. The subscores in the MCI group, includingMoCA total score （18.44 ± 5.60 vs. 27. 09 ±1.37; t= -12.422; P=0.000）, as well as visuoconstructional skills （2. 65 ± 1.39 vs. 4.49 ±0 . 74; t = - 9. 762; P = 0. 000）, attention （4.48 ± 1.70vs. 5. 89 ± 0. 31; t = 6. 706, P=0.000）,language （1.69 ± 0.80vs. 2. 41 ± 0. 95;t=4.893, P= 0.018）, abstraction （0.85 ± 0.69vs. 1.71 ± 0.53; t= -7.081, P=0.000）, delayed recall （1.29± 1.01 vs. 4. 04± 0. 99; t = 13. 824, P =0. 000） were significantly lower than those in the non-MCI group, and there were no significant differences in naming and orientation scores. In the MCI group, the subscores such as the MoCA total score in the LA＋ LI group （17.04 ±6. 15 vs. 21.04 ± 3.98; P〈 0.05）, as well as visuoconstructional skills （1.68 ± 1. 16 vs. 3.24 ± 1.13; P〈0. 05）, attention （3.92 ± 2. 03 vs. 5.19 ± 0. 87; P 〈0. 05）, delayed recall （1.35 ± 1.01 vs. 1.86 ±1.58; P 〈0. 05） were significantly lower than those in the LI group; the subscores such as the MoCA total score in the LA group （18. 18 ± 5.31 vs. 21.04 ± 3.98; 〈 =0.05）, as well as visuoconstructional skills （2.56 ±1.78 vs. 3.24 ±1.13; P〈0.05）, language （0.64 ± 0.23 vs. 1.24 ±0.83;P〈0.05）,delayedrecallO. 69 ± 0.58vs. 1.86 ±1.58;P〈0.01）were significantly lower than those in the LI group; the visuoconstmctional skills in the LA ＋ LI group was significantly lower than that in the LA group （1.68 ±1.16 vs. 2. 56 ± 1.78; P〈0. 05） and the LI group （1.68 ± 1.16 vs. 3.24± 1.13; P〈 0. 05）. Conclusions Hypertension, diabetes, hyperlipidemia, carotid atherosclerosis, smoking and the low level of education were the independent risk factors for MCI in patients with SVD. After SVD, the cognitive impairment in MCI presented as multiple cognitive domains impairments, including visuoconstructional skills and delayed recall. Cognitive impairment differed among the different types of SVD. Key words: Stroke; Brain Infarction; Cerebrovascular Disorders; Leukoaraiosis; Cognition Disorders; Neuropsychological Tests; Risk Factors
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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) | 0.000 | 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".