Comparison between mini mental state examination (MMSE) and Montreal cognitive assessment Indonesian version (MoCA-Ina) as an early detection of cognitive impairments in post-stroke patients
Bibliographic record
Abstract
Mild cognitive impairment (MCI) is defined as cognitive impairment that may never develop into dementia. Cognitive impairment is one long-term complication of a stroke. The Mini Mental State Examination (MMSE), which is commonly used as a screening tool for cognitive impairment, has a low sensitivity to detect cognitive impairment, especially MCI. Alternatively, the Montreal Cognitive Assessment Indonesian version (MoCA-Ina) has been reported to have a higher sensitivity than the MMSE. The aim of this study was to compare the proportion of MCI identified between the MMSE and MoCA-Ina in stroke patients. This was a cross-sectional study of stroke outpatients who attended the Polyclinic Neuromuscular Division, Rehabilitation Department, and Polyclinic Stroke, Neurology Department Cipto Mangunkusumo General Hospital, Jakarta. The proportion of MCI identified using the MMSE was 31.03% compared to 72.41% when using the MoCA-Ina. This difference was statistically significant (Fisher's exact test, p = 0.033). The proportion of MCI in stroke patients was higher when using the MoCA-Ina compared to the MMSE. The MoCA-Ina should be used as an alternative in the early detection of MCI in stroke patients, especially those undergoing rehabilitation.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".