7 The MOCA Versus Neuropsychological Testing in Assessing Presence of Memory Impairment and MCI
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».