Association of stroke risk profile and vascular cognitive impairment
Bibliographic record
Abstract
Objective To investigate the correlation between Framingham stroke risk profile(FSRP) and vascular cognitive impairment in stroke-free patients with cerebrovascular risk factors. Methods One hundred and eighty-four stroke-free subjects, selected from Zhejiang hospital, were divided into low risk group (56 subjects), moderate risk group (70 subjects) and high risk group (58 subjects) according to their FSRP score, and their cognitive function including memory ability, attention, executive function and language ability were assessed by Montreal cognitive assessment (MoCA), auditory verbal learning test(AVLT), digit symbol test, trail making test(TMT), digit span and verbal fluency test. Results The total MoCA scores which were (7.2±4.6), (13.8±3.9), (29.6±12.7)respectively, AVLT-delay recall scores which were(8.2±1.6), (6.7±1.4), (5.9±1.5)respectively, and digit symbol test score which were(34.7±9.3), (32.6±16.4), (29.7±13.6) respectively in low, intermediate and high risk groups, decreased with the increasing risk of stroke(P<0.05). The elapsed time in TMT-B which were (115.2±36.9)s, (147.6±44.8)s, (173.9±58.5)s respectively in low, intermediate and high risk groups, prolonged with the increasing risk of stroke (P<0.05). FSRP was associated with cognitive function, but inversely related to MoCA, AVLT-delay recall, digit symbol test, TMT-B and digit span fall back (P<0.05), but positively related to consuming time in TMT-B(P<0.05). Multivariate regression analysis showed that advanced age, hypertension, diabetes and smoking were the risk factors for vascular cognitive impairment(P<0.05). Conclusion Advanced age, smoking, hypertension and diabetes are the most important in vascular risk factors for cognitive impairment. Vascular risk factors can damage cognitive function with the increased risk of stroke, among which delayed recall and executive function are the main affected cognitive area. Key words: Vascular cognitive impairment; Framingham stroke risk profile; Risk factor
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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 | 0.002 |
| 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".