To Compare the Effect of Screening Cognitive Impairment of Post-Stroke Patients by MoCA and MMSE
Bibliographic record
Abstract
Objective:To compare the application of the MoCA(Montreal Cognitive Assessment,MoCA)and MMSE(mi-ni-mental state examination,MMSE)in screening cognitive impairment of post-stroke patients.Methods:Neuropsychological assessment was conducted to 65 patients with ischemic stroke within 14 days of the onset by MMSE and MoCA.12 of 65 patients were assessed by MoCA and MMSE within 14 days and after 3 to 6 months of onset respectively.With MMSE 23 points,MoCA 21 as the cut off value,the level of education less than 12 years added 1 point,illiteracy added 2 points.Results:The average score of MMSE was 25.2 ± 4.3,while that of MoCA was 18.6 ± 5.7 for 65 patients.37 patients showed cognitive impairment according to the assessment results by MoCA,but 19 of 37 patients(29%)showed normal score by MMSE.28 of 37 patients with normal score by MoCA also showed normal cognition by MMSE.Visual spatial,executive function,attention,and word repeating test impaired most;orientation and naming damaged less.In the 3 to 6 months follow-up period,1 patient with vascular dementia increased 1 point by MoCA,but did not change by MMSE.There were different rising up of the assessment score by MoCA and MMSE for the 5 patients with normal cognition,3 patients with mild cognitive impairment without dementia and 3 patients with moderate cognitive impairment without dementia.The average scores of executive and visual spatial in the two tests had no significant change.Conclusion:The sensitivity of MoCA is better than MMSE in screening the vas-cular cognitive impairment from the patients with acute cerebral infarction.The sensitivity of MoCA is higher than MMSE in detecting cognitive fluctuation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".