P1‐305: SENSITIVITY OF THE MONTREAL COGNITIVE ASSESSMENT TO AMYLOID PATHOLOGY IN A MIXED CLINICAL SAMPLE
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a widely-used screening tool for cognitive impairment and other cognitive disorders. Despite widespread use, there have been few investigations into correlations between MoCA and biomarkers of Alzheimer's disease (AD). In this study we looked at the relationship between MoCA performance and the presence of amyloid as detected by positron emission tomography (PET). Data from an ongoing longitudinal study of aging at the Lou Ruvo Center for Brain Health was used. Participants in this study include individuals with MCI or mild dementia, as well as an age-matched cognitively normal cohort (defined psychometrically). All individuals undergo amyloid PET scan. We included all individuals (age 55-90) with a CDR score of 0-1. The MoCA was administered within 6 weeks of the amyloid PET scan. 46 individuals had a positive amyloid scan while 57 were negative. Sensitivity and specificity for the total score were determined using amyloid positivity as the standard. A cutpoint of 25 yielded the best balance between sensitivity and specificity (74% and 74%, respectively). A total score of 27 was required to achieve 90% sensitivity to identify amyloid positive individuals (26 in individuals over the age of 75). None of the composite measures (or combinations or composite scores) increased accuracy over the total score. With the emergence of new diagnostic biomarkers, there is a need to define the utility of affordable, widely-available screening tools. In this mixed clinical sample, the MoCA score showed good sensitivity for detecting amyloid pathology but with low specificity. Thus the MoCA can be used to screen out individuals who are not at risk for AD pathology.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".