On the Clinimetrics of the Montreal Cognitive Assessment: Cutoff Analysis in Patients with Mild Cognitive Impairment due to Alzheimer’s Disease
Bibliographic record
Abstract
Background: In the era of disease-modifying therapies, empowering the clinical neuropsychologist's toolkit for timely identification of mild cognitive impairment (MCI) is crucial. Objective: Here we examine the clinimetric properties of the Montreal Cognitive Assessment (MoCA) for the early diagnosis of MCI due to Alzheimer's disease (MCI-AD). Methods: Data from 48 patients with MCI-AD and 47 healthy controls were retrospectively analyzed. Raw MoCA scores were corrected according to the conventional Nasreddine's 1-point correction and demographic adjustments derived from three normative studies. Optimal cutoffs were determined while previously established cutoffs were diagnostically reevaluated. Results: The original Nasreddine's cutoff of 26 and normative cutoffs (non-parametric outer tolerance limit on the 5th percentile of demographically-adjusted score distributions) were overly imbalanced in terms of Sensitivity (Se) and Specificity (Sp). The optimal cutoff for Nasreddine's adjustment showed adequate clinimetric properties (≤23.50, Se = 0.75, Sp = 0.70). However, the optimal cutoff for Santangelo's adjustment (≤22.85, Se = 0.65, Sp = 0.87) proved to be the most effective for both screening and diagnostic purposes according to Larner's metrics. The results of post-probability analyses revealed that an individual testing positive using Santangelo's adjustment combined with a cutoff of 22.85 would have 84% post-test probability of receiving a diagnosis of MCI-AD (LR+ = 5.06). Conclusions: We found a common (mal)practice of bypassing the applicability of normative cutoffs in diagnosis-oriented clinical practice. In this study, we identified optimal cutoffs for MoCA to be allocated in secondary care settings for supporting MCI-AD diagnosis. Methodological and psychometric issues are discussed.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".