The application of Montreal cognitive assessment (Chinese version) in diagnosing and assessing cognitive changes of mild cognitive impairment
Bibliographic record
Abstract
Objective To assess the effect of Montreal cognitive assessment (MoCA, Chinese version) in diagnosing and observing the cognitive changes of mild cognitive impairment (MCI). Methods The MoCA and Mini Mental State Examination (MMSE) were taken to all subjects (28 patients with MCI and 29 normal controls) to assess the effect and to compare the sensitivity and specificity of MMSE and MoCA in diagnosing MCI, and to compare the cognitive changes of the MCI patients at the beginning of study and 12 months later. Results The MoCA and MMSE are useful for differential diagnosis between normal control and MCI patients. MoCA was significant for the assessment of visuospacial/constructive abilities ( t = 2.151, P = 0.036), memory ( t = 4.704, P = 0.000), abstraction ( t = 2.787, P = 0.009) and orientation ( t = 3.162, P = 0.003) in comparison among groups. When the cut off point was 26, the sensitivity of MoCA and MMSE to diagnose MCI was 89.29% and 10.71% respectively, while the specificity was 82.76% and 100% respectively. The diagnostic sensitivity of MoCA was significantly higher than MMSE. In MCI patients, the score of MMSE and MoCA after 12 months were lower than baseline, the difference was significant in the MoCA total score ( t = 6.454, P = 0.000), visuospacial/constructive abilities ( t = 5.610, P = 0.000) and language ( t = 4.954, P = 0.000). Conclusion Comparing with MMSE, the sensitivity of MoCA is higher. The decline of the score of MoCA and visuospacial/constructive abilities and language may be a predictor in the conversion of MCI to dementia.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".