Identifying an Optimal Cutoff of the Montreal Cognitive Assessment to Predict Amyloid-PET Positivity in a Referral Memory Clinic
Bibliographic record
Abstract
BACKGROUND: Brain amyloid- positron emission tomography (PET) imaging is highly sensitive for identifying Alzheimer disease. Currently, there is a lack of insight on the association between amyloid-PET status and the widely used Montreal cognitive assessment (MoCA). Studying this relationship may optimize the clinical use of amyloid-PET imaging. OBJECTIVES: To evaluate the relationship between amyloid-PET status and MoCA scores and to identify a MoCA score cutoff that translates to amyloid-PET positivity. METHODS: Using retrospective chart review, patients from 2010 to 2017 with amyloid-PET scans (positive or negative) and MoCA test scores were included. We studied the relationship between amyloid-PET status and MoCA scores and the influence of age, sex, education, and race. A MoCA score cutoff for amyloid-PET positivity was estimated. RESULTS: Among the 684 clinic patients with dementia, 99 fulfilled inclusion criteria. Amyloid-PET positivity was associated significantly with lower MoCA scores (median=19, U=847, P=0.01). The MoCA score cutoff (25) used for minimal cognitive impairment (MCI) predicted amyloid-PET positivity suboptimally (sensitivity=94.6%, specificity=13.9%). A MoCA score cutoff of 20 patients had optimal sensitivity (64.2%) and specificity (67.4%). CONCLUSIONS: Amyloid-PET positivity is associated with lower MoCA scores. Clinical utility of amyloid-PET scan is likely to be suboptimal at the MoCA score cutoff for minimal cognitive impairment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".