Exploration of the Cut-off point of the Chinese version of the Montreal cognitive assessment among retired soldiers in Beijing
Bibliographic record
Abstract
Objective To explore the cut-off-point of the Chinese version of the Montreal cognitive assessment(MoCA)in patients of mild cognitive impairment(MCI)in Beijing.Methods One hundred and sixty nine people of army gerocomium meeting study criteria were divided into three groups,the MoCA and Mini Mental State Examination(MMSE)were taken to all subjects,to evaluate the correlation between them and the cut-off-point of MoCA for MCI.Results Score of MoCA closely correlated to MMSE(r=0.846,P0.001),according to the ROC curve and the Youden's index,the Sensitivity and specificity to distinguish MCI from normal controls were found to be 1.000 and 0.986 respectively,when the cut-off-point was 26.the Sensitivity and specificity to distinguish MCI and Dementia were found to be 0.932 and 0.717,when the cut-off-point was 19.Conclusions The MoCA is faily accurate at screening patients with MCI in the army gerocomium of Beijing,it was closely correlated to MMSE.To the subjects in our study,the score range of MoCA to diagnose MCI was from19 to 25.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".