The application of the Montreal cognitive assessment for elderly in China
Bibliographic record
Abstract
Objective To explore the cognitive changes of patients with MCI using the Montreal Cognitive Assessment(MOCA) for elderly preliminarily.Methods 85 normal controls (NC),117 subjects with mild cognitive impairment (MCI) and 73 patients with Alzheimer's disease (AD) were assessed with the MOCA.Results There were significant differences among the three groups in all items of MOCA(F=258.66,P＜0.01).Significant differences were observed in almost all the sub-tests between MCI group and NC group or MCI group and AD group(visuopatial F=54.86,P＜0.01;naming F=17.30,P＜0.01;attention F=82.50,P＜0.01;language F=25,88,P＜0.01;abstraction F=15.00 ,P＜0.01;delayed recall F=130.49,P＜0.01;orientation F=176.09,P＜0.01.).The most significant differences were found in delayed recall and orientation among three groups(F=176.09,P＜0.01;F=130.49,P＜0.01.).At 26 cut-point,The results of screening of the MOCA agree with the gold standard of clinical diagnose for the patient with MCI.The agreement rate for observation was 0.93.The agreement rate by chance was 0.61.The Kappa was 0.85.Conclusions The MOCA appropriately define MCI,NC and AD in their cognitive function,it has good discriminant validity.The test of delayed recall and orientation may be more sensitive in the detection of the older people.The MOCA is a useful screening instrument for the patient with MCI. Key words: Montreal Cognitive Assessment(MOCA); Mild cognitive impairment
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".