The use of MMSE and MoCA in patients with acute ischemic stroke in clinical
Bibliographic record
Abstract
BACKGROUND: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are brief cognitive screening tools that have been developed for the screening of patients with Mild Cognitive Impairment. METHODS: A total of 105 patients were included in this study, aged 53-89 years, with acute ischemic stroke admitted to hospital and fell into two groups: stroke patients with cognitive impairment (SCI) and controls with no cognitive impairment (n-SCI). The patient's characteristics are collected and regression analyses were performed to predict cognitive impairments. We use MMSE and MoCA assessment as prognostic indices for cognitive impairments of patient's with stroke. OBJECTIVES: Our aim was to examine the effectiveness of the MMSE and MoCA in screening cognitive impairments. MAIN RESULTS: There were significant difference among the two groups in the prevalence of diabetes mellitus (p < 0.05) and intracranial atherosclerosis (p < 0.05). A linear regression determined that the age, diabetes, intracranial atherosclerosis predicted the cognitive impairments. The ROC results for MoCA with an AUC of 0.882 and the corresponding results for MMSE show a similar AUC of 0.839. CONCLUSION: Neuropsychological performance of stroke patients was influenced by biological and demographic variables: age, diabetes and intracranial atherosclerosis. The MoCA and MMSE are both reliable assessments for the diagnosis of cognitive impairment after stroke.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".