Neuropsychological prediction of Alzheimer’s disease and functional abilities using brief screeners
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that is primarily characterized by memory impairment and decline in function. Patients often undergo lengthy neuropsychological evaluations that create testing fatigue and, ultimately, affect their validity. There are brief assessment options to assess probable AD that are not commonly explored in research and clinical settings. This study investigated the predictive 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 diagnosing AD. Additionally, this study examined the potential use of the ADAS-Cog-13 in clinical settings, as it is regarded as a measure for clinical trials. Data were analyzed from a sample of 533 individuals obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to explore the efficacy of these instruments in predicting the diagnosis of AD as well as the relationship between executive dysfunction, biological sex, and functional status as measured by the Functional Activities Questionnaire (FAQ). Results indicated that the MMSE supplemented with semantic fluency improved the predictive accuracy in identifying individuals with AD and demonstrated similar predictive utility to the MoCA. However, the MMSE combined with semantic fluency was not more sensitive than the ADAS-Cog-13 in predicting an AD diagnosis. Analyses examining functional outcomes revealed a weak but statistically significant positive correlation between executive dysfunction and functional difficulties. Results revealed no statistically significant difference between males and females on their performance on the FAQ and ADAS-Cog-13 subtest. Together, brief cognitive screeners, specifically the MMSE with the addition of a semantic fluency measure, can offer a practical approach to predict a diagnosis of AD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".