Validation of a modified Chinese version of Mini‐Addenbrooke's Cognitive Examination for detecting mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: For detecting mild cognitive impairment (MCI), brief cognitive screening tools are increasingly required for the advantage of time saving and no need for special equipment or trained raters. We aimed to develop a modified Chinese version of Mini-Addenbrooke's Cognitive Examination (C-MACE) and further evaluate its validation in detecting MCI. METHODS: A total of 716 individuals aged from 50 to 90 years old were recruited, including 431 cognitively normal controls (NC) and 285 individuals with MCI. The effect size of Cramer's V was used to explore which items in the Chinese version of Addenbrooke's Cognitive Examination-III (ACE-III-CV) best associated with MCI and to form the C-MACE. Receiver operating characteristic (ROC) analyses were carried out to explore the ability of C-MACE, ACE-III-CV, Chinese version of Montreal Cognitive Assessment-Basic (MoCA-BC), and Mini-Mental State Examination (MMSE) in discriminating MCI from NC. RESULTS: Five items with greatest effect sizes of Cramer's V were selected from ACE-III-CV to form the C-MACE: Memory Immediate Recall, Memory Delayed Recall, Memory Recognition, Verbal Fluency Animal and Language Naming. With a total score of 38, the C-MACE had a satisfactory classification accuracy in detecting MCI (area under the ROC curve, AUC = 0.892), superior to MMSE (AUC = 0.782) and comparable to ACE-III-CV (AUC = 0.901) and MoCA-BC (AUC = 0.916). In the subgroup of Age > 70 years, Education ≤ 12 years, the C-MACE got a highest classification accuracy (AUC = 0.958) for detecting MCI. CONCLUSION: In the Chinese-speaking population, C-MACE derived from ACE-III-CV may identify MCI with a good classification accuracy, especially in aged people with low education.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".