The Montreal Cognitive Assessment (MoCA-Ina) versus the Mini-Mental State Examination (MMSE-Ina) For Detecting Mild Cognitive Impairment among The Elderly
Bibliographic record
Abstract
Background: There are many neuropsychological instruments are used for screening cognitivefunctions in adults, with or without health problems such asthe MMSE-Ina and MoCA-Ina. Objectives:This study was designed to test the correlations and differences between MMSE-Inaand MoCA-Ina for early detection of decreasing cognitive function in the elderly. Methods: Total278 subjects were randomly selected from the 17 sub dictricts of Surakarta Municipality, CentralJava, Indonesia. Data collection was carried out in December 2018 and January 2019, withallsubjects individually interviewed using two cognitive tests (which lasted 30 – 45 minutes) alongwith physical and neurological examinations. The MMSE-Ina and MoCA-Ina scores of eachparticipant were correlated using the non-parametric Spearman rank test. Both scores werecompared based on level of education and gender. Results: The MoCa-Ina detected using MCIwas 215 (77.3%) while MMSE-Ina was 189 (68%), with 176 (63.3%) in severe 10 (3.5%).This study also showed a strong correlation between the MMSE-Ina and MoCA-Ina scores (r= 0.633 p < 0.000). The cut pointin this study were 23/24 for the MMSE-Ina and 25/26 for theMoCA-Ina which was less than 23 and 25,indicated cognitive impairment. Conclusion: TheMoCA-Ina is usedto screen cognitive impairment in the elderly. Bangladesh Journal of Medical Science Vol.20(1) 2021 p.164-169
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".