Optimal cut-off scores for the Mini Mental State Examination and Montreal Cognitive Assessment to detect MCI and dementia in Multiple System Atrophy
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) and dementia are reported in up to 44 % and 7 % of patients with Multiple system atrophy (MSA), respectively. The sensitivity and discriminative power of brief cognitive screening tools such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) for detecting MCI and dementia in MSA has not yet been evaluated. OBJECTIVE: The aim of this study was to determine the optimal cut-off scores of the MMSE and MoCA for accurately differentiating MSA patients with MCI and dementia from those with normal cognition. The fluency item of MoCA was also assessed separately for the same purpose. METHODS: Sixty-two MSA patients underwent a comprehensive II level neuropsychological evaluation, in order to diagnose dementia or MCI. ROC analyses were used to establish the optimal cut-off scores for MCI and dementia, respectively. RESULTS: According to the II level neuropsychological evaluation, 4.8 % of MSA patients met criteria for dementia and 53,2 % for MCI. The optimal MMSE cut-off scores were 20.5 for dementia (AUC = 0.915) and 26.5 for MCI (AUC = 0.698). For MoCA, the most accurate cut-offs were 14.0 to detect dementia (AUC = 0.919) and 19.5 to detect MCI (AUC = 0.702).ROC analysis suggested that both tests were more accurate to identify MCI than dementia. The optimal cut-off for MoCA fluency item to identify MCI was 8.5 words (AUC = 0.717). CONCLUSION: Our findings support MMSE and MoCA as effective and accessible tools to detect MCI and dementia in MSA. MoCA fluency item emerged as a reliable tool to detect MCI.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".