Usefulness of the Polish versions of the Montreal Cognitive Assessment 7.2 and the Mini-Mental State Examination as screening instruments for the detection of mild neurocognitive disorder
Bibliographic record
Abstract
INTRODUCTION: Screening tests are a key step in the diagnosis of dementia and should therefore be highly sensitive to the detection of mild neurocognitive disorders (NCD). The Mini Mental State Examination (MMSE) is the most commonly used screening method. The Montreal Cognitive Assessment (MoCA) is a newer and less well-known screening tool, which has none of the limitations of the MMSE. AIM: The aim of this study was to analyse the reliability of the Polish versions of MoCA 7.2 vs MMSE in the detection of mild NCD among people aged over 60. MATERIAL AND METHODS: The study was carried out at the Department and Clinic of Geriatrics from September 2014 to March 2017. The study included 281 participants, 91 of whom were assigned to the group without NCD. The other 190 had been diagnosed with mild NCD. RESULTS: In the analysis of the ROC curve of the MoCA 7.2 results, the AUC was 0.925 (p < 0.001). The optimal cut-off point for mild NCD was 23/24 points, with sensitivity and specificity of 83.2% and 79.1%. In the ROC curve of MMSE results, the AUC was 0.847 (p < 0.001). The optimal cut-off point for mild NCD was 27/28 points, with sensitivity and specificity of 75.8% and 66.7%. The difference between AUC MoCA 7.2 and MMSE was 0.078 (p = 0.036). CONCLUSIONS: MoCA 7.2 detects mild NCD with more sensitivity than MMSE. We recommend using the cut-off point for MoCA of 23/24 points, because this is characterised by a higher sensitivity than the previously recommended cut-off point of 25/26 points. For the MMSE, the recommended cut-off point should be 27/28, which gives greater diagnostic accuracy than the previously recommended 25/26 points.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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".