Comparative accuracies of two common screening instruments for classification of Alzheimer's disease, mild cognitive impairment, and healthy aging
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to compare the utility and diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in a clinical cohort. METHODS: Three hundred twenty-one AD, 126 MCI, and 140 older adults with healthy cognition (HC) were evaluated using the MMSE, the MoCA, a standardized neuropsychologic battery according to the Consortium to Establish a Registry of Alzheimer's Disease (CERAD-NB), and an informant-based measure of functional impairment, the Dementia Severity Rating Scale (DSRS). Diagnostic accuracy and optimal cut-off scores were calculated for each measure, and a method for converting MoCA to MMSE scores is presented. RESULTS: The MMSE and MoCA offer reasonably good diagnostic and classification accuracy as compared with the more detailed CERAD-NB; however, as a brief cognitive screening measure, the MoCA was more sensitive and had higher classification accuracy for differentiating MCI from HC. Complementing the MMSE or the MoCA with the DSRS significantly improved diagnostic accuracy. CONCLUSION: The findings support recent data indicating that the MoCA is superior to the MMSE as a global assessment tool, particularly in discerning earlier stages of cognitive decline. In addition, we found that overall diagnostic accuracy improves when the MMSE or MoCA is combined with an informant-based functional measure. Finally, we provide a reliable and easy conversion of MoCA to MMSE scores. However, the need for MCI-specific measures is still needed to increase the diagnostic specificity between AD and MCI.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".