Application of Montreal cognitive assessment cut-off in screening mild cognitive impairment
Bibliographic record
Abstract
Objective To study the application of Montreal cognitive assessment(MoCA) in screening mild cognitive impairment(MCI) and its optimal cut-off value.Methods One hundred and fifty -three MCI patients were divided into control group(n= 69),MCI group(n= 60),and Alzheimer's disease(AD) group(n= 24) according to its diagnostic criteria and evaluated using MoCA and mini-mental state examination(MMSE).Correlation between the MoCA and MMSE scores for the patients was analyzed.Sensitivity,specificity,Kappa value and Youden index of MoCA in screening MCI patients were calculated to select its optimal cut-off value.Results The MMSE and MoCA scores were significantly lower in MCI and AD groups than in control group(P 0.05).The MMSE score was closely related with the MoCA score(r=0.847,P0.01).The sensitivity, specificity and Kappa value of MoCA for the diagnosis of MCI were 98.3%,85.5%and 0.830 respectively when its cut-off value was 26.ROC curve showed that the sensitivity,specificity and Kappa value of MoCA for the diagnosis of MCI were 93.3%,97.1%and 0.906 when its cut-off value was 25.Conclusion MMSE and MoCA scores are closely related in MCI patients and consistent with their clinical diagnosis.The cut-off value of MoCA at 25 is recommended for the diagnosis of MCI.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".