Supplementary Material for: Comparative Diagnostic Accuracy of the ACE-III, MIS, MMSE, MoCA, and RUDAS for Screening of Alzheimer Disease
Bibliographic record
Abstract
Background: Our aim was to evaluate and compare the diagnostic properties of 5 screening tests for the diagnosis of mild Alzheimer disease (AD). Methods: We conducted a prospective and cross-sectional study of 92 patients with mild AD and of 68 healthy controls from our Department of Neurology. The diagnostic properties of the following tests were compared: Mini-Mental State Examination (MMSE), Addenbrooke's Cognitive Examination III (ACE-III), Memory Impairment Screen (MIS), Montreal Cognitive Assessment (MoCA), and Rowland Universal Dementia Assessment Scale (RUDAS). Results: All tests yielded high diagnostic accuracy, with the ACE-III achieving the best diagnostic properties. The area under the curve was 0.897 for the ACE-III, 0.889 for the RUDAS, 0.874 for the MMSE, 0.866 for the MIS, and 0.856 for the MoCA. The Mini-ACE score from the ACE-III showed the highest diagnostic capacity (area under the curve 0.939). Memory scores of the ACE-III and of the RUDAS showed a better diagnostic accuracy than those of the MMSE and of the MoCA. All tests, especially the ACE-III, conveyed a higher diagnostic accuracy in patients with full primary education than in the less educated group. Implementing normative data improved the diagnostic accuracy of the ACE-III but not that of the other tests. Conclusions: The ACE-III achieved the highest diagnostic accuracy. This better discrimination was more evident in the more educated group.
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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 | 0.002 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.846 | 0.269 |
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".