Impact of the Prevalence of Cognitive Impairment on the Accuracy of the Montreal Cognitive Assessment
Bibliographic record
Abstract
OBJECTIVES: The focus of this study is the classification accuracy of the Montreal Cognitive Assessment (MoCA) for the detection of cognitive impairment (CI). Classification accuracy can be low when the prevalence of CI is either high or low in a clinical sample. A more robust result can be expected when avoiding the range of test scores within which most classification errors are expected, with adequate predictive values for more clinical settings. METHODS: The classification methods have been applied to the MoCA data of 5019 patients in the Uniform Data Set of the University of Washington's National Alzheimer's Coordinating Center, to which 30 Alzheimer Disease Centers (ADCs) contributed. RESULTS: The ADCs show sample prevalence of CI varying from 0.22 to 0.87. Applying an optimal cutoff score of 23, the MoCA showed for only 3 of 30 ADCs both a positive predictive value (PPV) and a negative predictive value (NPV) ≥0.8, and in 18 cases, a PPV ≥0.8 and for 13 an NPV ≥0.8. Overall, the test scores between 22 and 25 have low odds of true against false decisions of 1.14 and contains 55.3% of all errors when applying the optimal dichotomous cut-point. Excluding the range 22 to 25 offers higher classification accuracies for the samples of the individual ADCs. Sixteen of 30 ADCs showed both NPV and PPV ≥0.8, 25 show a PPV ≥0.8, and 21 show an NPV ≥0.8. CONCLUSION: In comparison to a dichotomous threshold, considering the most error-prone test scores as uncertain enables a classification that offers adequate classification accuracies in a larger number of clinical settings.
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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 | 0.030 | 0.220 |
| 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 | 0.002 |
| 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".