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
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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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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".