Development of a computerized tool for the chinese version of the montreal cognitive assessment for screening mild cognitive impairment
Bibliographic record
Abstract
Background:The Montreal Cognitive Assessment (MoCA) is used for screening mild cognitive impairment (MCI), and the Beijing version (MoCA-BJ) is widely used in China. We aimed to develop a computerized tool for MoCA-BJ (MoCA-CC).Methods:MoCA-CC used person-machine interaction instead of patient-to-physician interaction; other aspects such as the scoring system did not differ from the original test. MoCA-CC, MoCA-BJ and routine neuropsychological tests were administered to 181 elderly participants (MCI = 96, normal controls [NC] = 85).Results:A total of 176 (97.24%) participants were evaluated successfully by MoCA-CC. Cronbach's α for MoCA-CC was 0.72. The test–retest reliability (retesting after six weeks) was good (intraclass correlation coefficient = 0.82; P < 0.001). Significant differences were observed in total scores (t = 9.38, P < 0.001) and individual item scores (t = 2.18–8.62, P < 0.05) between the NC and MCI groups, except for the score for “Naming” (t = 0.24, P = 0.81). The MoCA-CC total scores were highly correlated with the MoCA-BJ total scores (r = 0.93, P < 0.001) in the MCI participants. The area under receiver–operator curve for the prediction of MCI was 0.97 (95% confidence interval = 0.95–1.00). At the optimal cut-off score of 25/26, MoCA-CC demonstrated 95.8% sensitivity and 87.1% specificity.Conclusion:The MoCA-CC tool developed here has several advantages over the paper-pencil method and is reliable for screening MCI in elderly Chinese individuals, especially in the primary clinical setting. It needs to be validated in other diverse and larger populations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".