Comparison of the effectiveness of the Montreal Cognitive Assessment 7.2 and the Mini-Mental State Examination in the detection of mild neurocognitive disorder in people over 60 years of age. Preliminary study.
Bibliographic record
Abstract
OBJECTIVES: Analysis of reliability of the Polish version of the MoCA 7.2 vs. the MMSE in mild NCD detecting, while taking into consideration the sensitivity and specificity of cut-off points for each type of education. METHODS: Cross-sectional study was conducted at the Department of Geriatrics, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun. The study was conducted between September 2014 and December 2015. The study involved 131 participants, including 54 people assigned to the group without NCD and 77 to the group with mild NCD. Recruitment for both groups was performed on the basis of specific inclusion and exclusion criteria. RESULTS: Mean scores of the MoCA 7.2 and the MMSE showed a statistically significant difference between the groups with and without mild NCD. The optimal cut-off point on the MoCA scale for mild NCD was 24/25. The optimal cut-off point on the MMSE scale for mild NCD was 28/29. In the ROC curve analysis, area under the curve (AUC) for the MoCA was significantly greater than the AUC for the MMSE. CONCLUSIONS: The MoCA 7.2 detect mild NCD with greater sensitivity than the MMSE. In the case of this tool, we propose the use of 24/25 cut-off point which has a higher sensitivity than the recommended 25/26 cut-off point. The MoCA 7.2 therefore can be used by primary healthcare and in the geriatric practice as a screening tool in detecting early cognitive impairment.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".