The application of Mini-Mental State Examination and Montreal cognitive assessment for mild cognitive impairment and dementia in community survey
Bibliographic record
Abstract
Objective To compare the accuracy,sensitivity,specificity of the Mini-Mental State Examination (MMSE) and Montreal cognitive assessment (MoCA) in screening of mild cognitive impairment(MCI) and dementia in community survey. Methods This study was conducted among residents aged 65 years and above in the urban and rural areas selected by stratified sampling from 6 urban and 2 rural communities.The 2 111 cases who finished the Neuropsychological tests included MMSE,MoCA,and Clinical Dementia Rating scale(CDR) were divided into groups normal control,MCI,and dementia.The accuracy,sensitivity and specificity was compared by the area under the curve (AUC) of receiver-operating characteristic curve.The clinical diagnoses of MCI was made according to Petersen′s criteria. Results The areas under curve were between 0.72 to 0.99 for MMSE and MoCA on discriminated MCI or dementia (Z=2.75，P 0.05). In MCI survey,the cutoff values and sensitivity/specificity of MMSE vs.MoCA in education levels of illiterate,primary,secondary and above were 21(84%/64%) vs.15(88%/54%),26(91%/70%) vs.20(94%/68%),27(93%/86%) vs.23(93%/80%) respectively;but in dementia survey,the cutoff values and sensitivity/specificity of MMSE vs. MoCA in education levels of illiterate,primary,secondary and above were 16(98%/85%) vs.11(98%/70%),20(100%/94%) vs.14(100%/87%),22(100%/98%) vs.16(100%/95%) respectively. Conclusions MMSE and MoCA are good cognitive assessment tools in the survey of MCI or dementia with high accuracy,sensitivity and specificity.But the cutoff value is different according to one′s education level.MoCA may be more suitable for MCI survey but MMSE for dementia. Key words: Cognition disorders; Dementia; Mini-Mental State Examination; Montreal cognitive assessment
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".