Usefulness of MoCA in detecting preclinical AD
Bibliographic record
Abstract
BACKGROUND: Individuals with preclinical AD are difficult to detect using traditional clinical tools. Yet, many individuals classified as cognitively unimpaired (CU) can exhibit a heterogeneous pattern of subtle clinical abnormalities, potentially linked to early Alzheimer's disease (AD). Primary care providers often have access only to clinical tests to study at-risk community-dwelling older adults who visit them without cognitive complaints. Since early intervention is crucial in AD, identifying clinical tools to detect preclinical AD is important in areas with limited access to blood or imaging biomarkers. In this study, we evaluate the link between Montreal Cognitive Assessment (MoCA) total scores and AD pathology in CU individuals. METHODS: F-MK6240 scans. These individuals were stratified based on their amyloid PET status, determined by visual reading, into CU A+ (n = 35) and CU A- (n = 137), as part of the ongoing HEAD study. CU adults had Clinical Dementia Rating (CDR) of 0 and were clinically identified as non-MCI and non-demented. Voxel-wise linear regression models tested the association between MoCA total scores and tau pathology. RESULTS: F-FTP [Figure 2]. When we separated the population by Aβ status, we found that these results were driven by the CU A+ group (Figure 3A) and were not present in CU A- individuals (Figure 3B). DISCUSSION: These findings indicate that the MoCA, a simple routine in-office clinical test, can detect subtle cognitive dysfunction associated with preclinical AD. This suggests that simple cognitive testing can play a role in the early detection of AD and therefore an option for prescreening older adults in non-specialized clinical settings, the initial interface for most older adults without cognitive complaints. CONCLUSION: MoCA total, the common dementia screening test also plays a valuable role in detecting preclinical 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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".