Cognitive impairment in patients with chronic cerebral circulation insufficiency assessed by MMSE and MoCA scale
Bibliographic record
Abstract
Objective To assess the cognitive impairment characteristic in patients with chronic cerebral circulation insufficiency( CCCI) and the sensitivity and correlation of the Mini-Mental State Exam( MMSE) and Montreal Cognitive Assessment( MoCA) scales. Methods We selected 63 cases patients with CCCI in May 2009 to March 2010,from neurology of the first hospital Jilin University as case group. Meanwhile we selected 55 cases the elderly with no obvious difference was found as control group. Then through MMSE and MoCA scales,we aseessed the cognitive impairment characteristics and the sensitivity and correlation of two scales. Results( 1) Compared with the control group by MoCA scale,cognitive impairment scores in the visuospatial and executive ability,language and naming and delayed recall in case group were lower than that of the control group. The difference was statistically significant( P < 0. 05).( 2) In terms of cognitive impairment detection rate,MoCA scale was significantly greater than MMSE scale. The difference was statistically significant( P < 0. 01).Meanwhile we think the MoCA 21 score may be as a demarcation point between light and heavy level of cognitive impairment.Conclusion( 1) Cognitive impairment characteristic in patients with CCCI was visuospatial and executive ability,language and naming and delayed recall.( 2) Compared with MMSE scale,MoCA scale is easier to detect cognitive impairment of patients with CCCI. Meanwhile we think the MoCA 21 score as a demarcation point may differentiate light from heavy level of cognitive impairment. So patients with CCCI are beneficial to early detection in clinical,early prevention and treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".