Comparison of the Diagnostic Accuracy of Neuropsychological Tests in Differentiating Alzheimer's Disease from Mild Cognitive Impairment: Can the Montreal Cognitive Assessment Be Better than the Cambridge Cognitive Examination
Bibliographic record
Abstract
OBJECTIVE: Considering the lack of studies on measures that increase the diagnostic distinction between Alzheimer's disease (AD) and mild cognitive impairment (MCI) and on the role of the Cambridge Cognitive Examination (CAMCOG) in this, our study aims to compare the utility of the CAMCOG, Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in helping to differentiate AD from MCI in elderly people with >4 years of schooling. METHOD: A total of 136 elderly subjects - 39 normal controls as well as 52 AD patients and 45 MCI patients treated at the Institute of Geriatrics and Gerontology, Porto Alegre, Brazil - were assessed using the MMSE, CAMCOG, clock drawing test (CDT), verbal fluency test (VF), Geriatric Depression Scale and Pfeffer Functional Activities Questionnaire. RESULTS: The results obtained by means of a receiver operating characteristic curve showed that the MoCA is a better screening test for differentiating elderly subjects with AD from those with MCI than the CAMCOG and MMSE as well as other tests such as the CDT and VF. CONCLUSION: The MoCA, more than the CAMCOG and the other tests, was shown to be able to differentiate AD from MCI, although, as Roalf et al. [Alzheimers Dement 2013;9:529-537] pointed out, further studies might lead to measures that will improve this differentiation.
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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 | 0.012 | 0.036 |
| 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".