Chinese version of Montreal Cognitive Assessment Basic for discrimination among different severities of Alzheimer’s disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To find out whether the Chinese version of Montreal Cognitive Assessment Basic (MoCA-BC) and its subtests could be applied in discrimination among cognitively normal controls (NC), mild cognitive impairment (MCI), mild and moderate Alzheimer's Disease (AD), and furthermore, to determine the optimal cutoffs most sensitive to distinguish between them. DESIGN: A cross-sectional validation study. SETTING: Huashan Hospital, Shanghai, China. PARTICIPANTS: There was a total of 1,969 participants: individuals with MCI (n=663), mild (n=345), moderate (n=441) AD, and cognitively NC (n=520) were recruited from the Memory Clinic, Huashan Hospital, Shanghai, China. MEASUREMENTS: Baseline MoCA-BC scores were collected from firsthand data. Two subtests were calculated from MoCA-BC: the Memory Index Score of MoCA-BC (MoCA-BC-MIS) and the Non-memory Index Score of MoCA-BC (MoCA-BC-NM). RESULTS: MoCA-BC was an effective cognitive tool to discriminate among NC, MCI, mild and moderate AD in the Chinese elderly across all education groups, implying that it was efficient not only for detecting MCI, but for different severities of AD as well. For MCI screening, the total score of MoCA-BC (MoCA-BC-T) and MoCA-BC-MIS had similar high sensitivity and specificity. For discrimination among MCI, mild and moderate AD, the MoCA-BC-T and MoCA-BC-NM had similar performance. CONCLUSION: MoCA-BC is an effective cognitive test to distinguish between NC, MCI, mild and moderate AD among the Chinese elderly with various levels of education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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