Neuropsychological prediction of Alzheimer’s disease and functional abilities using brief screeners
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".