Neuropsychological prediction of Alzheimer’s disease and functional abilities using brief screeners
Notice bibliographique
Résumé
Introduction: Alzheimer’s disease (AD) is a neurodegenerative disease that is primarily characterized by memory impairment, deficits in other cognitive domains, and decline in functional status. According to the Alzheimer’s Association, approximately 6.7 million Americans, age 65 and older, were living with AD in 2023. Patients often undergo lengthy neuropsychological evaluations that creates testing fatigue and ultimately affects the validity of results. Extensive literature also suggests that sex differences affect appropriate AD diagnosis in that women are more likely to demonstrate more impairment in instrumental activities of daily living (IADLs). Objective: The purpose of this study is to investigate the ability of the Mini-Mental State Examination (MMSE) with the addition of a semantic fluency measure compared to the Montreal Cognitive Assessment (MoCA) and the Alzheimer’s Disease Assessment Scale-13 item Cognitive subscale (ADAS-Cog-13) in predicting a diagnosis of AD. Additionally, the current study aims to examine the potential clinical use of the ADAS-Cog-13 in detecting changes in functional abilities using the Functional Abilities Questionnaire (FAQ). Methods: Permission has been granted by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to use their de-identified archival data to explore the efficacy of these instruments in predicting a diagnosis of AD as well as the relationship between functional decline, executive functioning, and biological sex. Participants will include individuals aged 65 years or older that are cognitively intact and formally diagnosed with AD in an initial screening visit. Statistical analyses will then be used to determine significance at a p-level less than or equal to 0.05. Results: It is hypothesized that the MMSE supplemented with a semantic fluency task will predict a diagnosis of AD, demonstrate similar predictive ability to the MoCA in identifying individuals with AD, and be more sensitive than the ADAS-Cog-13 in predicting AD diagnosis. It is postulated that higher ADAS-Cog-13 scores on an executive functioning item will correlate with greater impairments in IADLs as measured by the FAQ, and that women will exhibit greater impairments on both the ADAS-Cog-13 and FAQ than men. Discussion: The current study will address the limitation of the MMSE lacking an executive function measure, which has made the MoCA historically more superior in detecting cognitive impairment. In regards to functional decline, the ADAS-Cog-13 will also provide clinical insight into its potential in assessing IADL impairment and sex differences. Findings will shed light on the potential benefits of various approaches to brief cognitive assessment in clinical practice among different disciplines including neuropsychology, primary care, psychiatry, and neurology. Such providers can use such tasks to grant earlier diagnoses and interventions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,005 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 tête enseignante, 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 ».