Diagnostic utility of the Addenbrooke's Cognitive Examination – III (ACE‐III), Mini‐ACE, Mini‐Mental State Examination, Montreal Cognitive Assessment, and Hasegawa Dementia Scale‐Revised for detecting mild cognitive impairment and dementia
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Bibliographic record
Abstract
BACKGROUND: Early detection of mild cognitive impairment (MCI) and dementia is important to promptly start appropriate intervention. However, it is difficult to examine a patient using long and thorough cognitive tests in a general clinical setting. In this study, we aimed to investigate the diagnostic validity of the Addenbrooke's Cognitive Examination - III (ACE-III), Mini-ACE (M-ACE), Montreal Cognitive Assessment (MoCA), Hasegawa Dementia Scale-Revised (HDS-R), and Mini-Mental State Examination (MMSE) to identify MCI and dementia. METHODS: A total of 249 subjects (controls = 50, MCI = 94, dementia = 105) at a memory clinic participated in this study, and took the ACE-III, M-ACE, MoCA, HDS-R, and MMSE. After all examinations had been carried out, a conference was held, and the clinical diagnoses were established. RESULTS: The areas under the curve (AUC) of the ACE-III, M-ACE, MoCA, HDS-R, and MMSE for diagnosing MCI were 0.891, 0.856, 0.831, 0.808, and 0.782. The AUC of the ACE-III was significantly larger than those of the MoCA, HDS-R, and MMSE. The AUCs of the ACE-III, M-ACE, MoCA, HDS-R, and MMSE for diagnosing dementia were 0.930, 0.917, 0.854, 0.871, and 0.856. Thus, the AUCs of the ACE-III and M-ACE were significantly larger than those of the MoCA, HDS-R, and MMSE. CONCLUSION: The ACE-III is a useful cognitive instrument to detect MCI. For distinguishing dementia patients from non-dementia patients, the ACE-III and M-ACE are superior to the MoCA, HDS-R, and MMSE.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it