7 The MOCA Versus Neuropsychological Testing in Assessing Presence of Memory Impairment and MCI
Bibliographic record
Abstract
Objective: The Montreal Cognitive Assessment (MOCA) is a brief cognitive screener, widely used by providers to detect mild cognitive impairment (MCI). It encompasses 30 questions, assessing executive functioning, visuospatial skills, language, memory, attention, and orientation. Although the MOCA has been shown to have high sensitivity (90%) and specificity (87%) for detecting MCI, existing studies have primarily included participants who were already diagnosed with amnestic MCI via neuropsychological testing. Since several factors beyond the presence of MCI can contribute to low performance on the MOCA (e.g., premorbid IQ, fatigue, mood symptoms), over-reliance on the MOCA runs the risk of falsely identifying individuals as having cognitive impairment. The MOCA’s memory subtest raises particular concern as there are several language-based tasks between the learning and delay trials, introducing the potential for interference effects. Thus, the MOCA’s ability to accurately identify those at risk for MCI in the community remains unclear. The objective of the present study was to evaluate: (1) the MOCA’s association with neuropsychological memory measures; and (2) its ability to distinguish between neurocognitive groups (intact vs. MCI vs. dementia). Participants and Methods: This study involved a retrospective analysis of fifty-one patients (M age=72.58 [7.90]; M education= 16.37 [16.37]) who underwent neuropsychological evaluation. Standardized scores for total list-learning (HVLT; CVLT-bf) were used to capture memory encoding; retention % scores were used to capture memory storage. MOCA scores included Total MOCA, MOCA-Orientation, and the MOCA Memory Index (MOCA-MEM). MOCA-MEM was calculated based on Julayanont et al., 2014— (Free-Delayed Recall*3) + (Category-Cued Recall*2) + Multiple Choice-Cued Recall. Bivariate correlations were conducted for the MOCA and neuropsychological test scores. Participants were divided into three diagnostic groups, classified by the neuropsychologist: (1) Cognitive Intact (CI; n=13); (2) MCI (n=26); and (3) Major Neurocognitive Disorder/Dementia (MNCD; n=11). Analysis of covariance was used to analyze differences between the cognitive groups on Total MOCA, MOCA-Orientation, and MOCA-MEM. Results: Total MOCA correlated with word-list learning (r=.434, p=.004) and retention% (r=.306, p=.049). MOCA-MEM was correlated with word-list learning (r=.367, p=.042); it did not significantly correlate with retention%. MOCA-Orientation had the strongest correlation with retention0/) (r=.406, p=.009). Means of Total MOCA significantly differed between CI (25.31[2.56]), MCI (22.04[4.14]), and MNCD (15.44[4.13]). MOCA-MEM only differentiated CI (10[3.66]) and MNCD (5.71[2.14]); it did not differentiate MCI (6.94[3.13]) from either CI or MNCD. Conclusions: Our findings suggest that the MOCA has limitations in accurately classifying memory deficits in older adults. First, our study suggests that the MOCA-MEM reflects encoding rather than memory storage. Given that deficiency in encoding may be secondary to other cognitive deficits, such as attention and executive dysfunction, performance on MOCA-MEM cannot readily delineate the presence of an amnestic process. Second, the findings show that MOCA-MEM does not differentiate between patient groups with intact cognition versus MCI, nor those with MCI versus MNCD. These findings argue the importance of neuropsychological evaluation in deciphering patterns of memory performance and the presence of an amnestic process.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".