[The application of Montreal cognitive assessment in urban Chinese residents of Beijing].
Bibliographic record
Abstract
OBJECTIVE: To study the distribution of Montreal cognitive assessment (MoCA) scores in terms of age and educational level in Chinese residents aged 50 years and over and to offer a benchmark for a cutoff score. METHODS: A total of 281 residents aged 50 years or older was drawn randomly in the urban areas of Beijing, including 215 healthy elderly controls (NC) and 66 patients meeting the clinical criteria of mild cognitive impairment (MCI). The final scores for MoCA were given in the form of mean percentage distributions specific for age, sex and educational level so as to compare the validity of MMSE mini-mental state examination and MoCA in detecting MCI. By a fitting multiple regression model the influence of the factors on MMSE and MoCA was assessed. RESULTS: Using a cutoff score of 26, MMSE had a sensitivity of 24.2% to detect MCI, whereas MoCA detected 92.4% of the MCI subjects. We found a bivariate correlation between MoCA scores and both the factors of age and educational level (P < 0.001). CONCLUSIONS: MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing normally on MMSE. Our adjustment in the cutoff scores would improve the detection of MCI and Alzheimer's disease by reducing the number of false negatives. MoCA scores should be used to identify current cognitive difficulties but not to make formal diagnoses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".