CAN MOCA SCORES PREDICT AMYLOID PET SCAN POSITIVITY? SENSITIVITY AND SPECIFICITY ANALYSES IN A MEMORY CLINIC SAMPLE
Bibliographic record
Abstract
Background: Sensitivity and specificity of Montreal cognitive assessment (MOCA) score to predict amyloid positive scans is clinically meaningful. Objectives: Estimate sensitivity and specificity of MOCA scores for predicting positive amyloid PET scans. Methods: Memory clinic patients from July 2010 to Dec 2017 with available MOCA score and binary amyloid-PET result were analyzed retrospectively. Univariate and multivariate analyses of age, race, gender and education with MOCA score and amyloid-PET status was performed. Sensitivity and specificity of MOCA score to predict amyloid positivity was calculated. Receiver Operating Conditions (ROC) analysis measured area under the curve (AUC) and selected the ideal cut point. Results: From July 2010 to Dec 2017, 99 patients had available MOCA scores and Amyloid-PET imaging. Mean±SD of age, education, MOCA score of subjects were 71.31 ± 9 years, 13.33 ± 2.52 years and 20.09 ± 4.84 respectively. 49 females (49.5%), 93 Caucasians (94%) and 56 amyloid-PET positive (56.57%) patients were included. Lower MOCA scores significantly correlated to amyloid positivity in univariate (χ2=6.39, df=1, p<0.05) and multivariate logistic regression analyses after adjusting for age, gender, education, and race (OR= 0.89, 95% CI= 0.80–0.99, p<0.05). The established clinical cut point of MOCA<26, had 98.2% sensitivity and 9.3% specificity for predicting Amyloid-PET positivity. AUC was 0.65 (95% CI 0.54 - 0.76). The ideal MOCA cut point score was 20, with 67% sensitivity and 64% specificity. Conclusions: A real-world estimate confirms poor clinical utility of MOCA to predict amyloid positivity. The clinical cut point was not adequately specific. Additional studies are needed to detect the pre-clinical stage of dementia.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".