Validation of the Chinese version of Addenbrooke’s cognitive examination III for detecting mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVES: To evaluate the reliability and validity of Chinese version of Addenbrooke's Cognitive Examination III (ACE-III-CV) in the identification of mild cognitive impairment (MCI), and further investigate the optimal cutoff scores according to different age and education level. METHOD: A total of 716 individuals aged from 50 to 90 years old were recruited through internet-based and print advertisements, including 431 cognitively normal controls (NC) and 285 individuals with MCI according to an actuarial neuropsychological method put forward by Jak and Bondi. Besides the cognitive screening tests of ACE-III-CV, Mini-Mental State Examination (MMSE) and Chinese version of Montreal Cognitive Assessment-Basic (MoCA-BC), all the participants underwent a battery of standardized neuropsychological tests. Validations of the ACE-III-CV, MMSE, and MoCA-BC for detecting MCI from NC were determined by Receiver operating characteristic (ROC) curves. RESULTS: ACE-III-CV had a good reliability (Cronbach's coefficient α = 0.807, intraclass correlation coefficients for interrater and test-retest reliability were 0.95 and 0.93). According to the area under ROC curve (AUC), ACE-III-CV and MoCA-BC showed better ability than MMSE in detecting MCI. No significant difference was found between ACE-III-CV and MoCA-BC. The optimal cutoff scores of ACE-III-CV for screening MCI were 72 for individuals with 1-9 years of education, 78 for individuals with 10-15 years of education, and 80 for individuals with more than 16 years of education. CONCLUSION: The Chinese version of ACE-III-CV is a reliable and valid screening tool for detecting MCI. The optimal cutoff scores are closely related with education level.
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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 | 0.007 | 0.013 |
| 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 | 0.001 |
| 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".