Incidence and risk factors of cognitive impairment in patients with vascular risk factors
Bibliographic record
Abstract
Objective To screen the patients with vascular risk factors from clinic by applying self-identification and screening stools,discuss the incidence and risk factors of cognitive impairment,and provide evidence for early identification of vascular cognitive impairment(VCI).Methods Taking Mini-Mental State Examination(MMSE) and Montreal Cognitive Assessment(MoCA) as diagnosis basis,601 patients with vascular risk factors were divided into normal group,mild cognitive impairment group(mVCI group) and vascular dementia group(VD group).The incidence and risk factors of cognitive impairment were analyzed.Results Among 601 patients,230 in normal group,215 in mVCI group and 165 in VD group.There were 380 patients with cognitive impairment in 610 patients(62.3%).The sexual distribution was treated with chi-square test in three groups and results showed no significant difference in ratio of male and female(P0.05).The age distribution was treated with variance analysis and the result showed a statistical significance(P0.01).Conclusion The screening has found that most of patients have cognitive impairment in varying degrees,which indicates it is very important that early identification and screening for cognitive function in the patients with vascular risk factors.The sex was not risk factor but age is one of risk factors in the patients with vascular risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".