Montreal Cognitive Assessment Is Superior to Standardized Mini-Mental Status Exam in Detecting Mild Cognitive Impairment in the Middle-Aged and Elderly Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
AIM: This study compares the usefulness of Montreal Cognitive Assessment (MoCA) to Standardized Mini-Mental Status Exam (SMMSE) for diagnosing mild cognitive impairment (MCI) in Type 2 diabetes mellitus (DM) population. METHODS: This prospective pilot study enrolled 30 community dwelling adults with Type 2 DM aged 50 years and above. Subjects were assessed using both the SMMSE and MoCA for MCI. In all subjects, depression and dementia were ruled out using the DSM IV criteria, and a functional assessment was done. MCI was diagnosed using the standard test, the European consortium criteria. Sensitivity and specificity analysis, positive and negative predictive values, likelihood ratios and Kappa statistic were calculated. RESULTS: In comparison to consortium criteria, the sensitivity and specificity of MoCA were 67% and 93% in identifying individuals with MCI, and SMMSE were 13% and 93%, respectively. The positive and negative predictive values for MoCA were 84% and 56%, and for SMMSE were 66% and 51%, respectively. Kappa statistics showed moderate agreement between MoCA and consortium criteria (kappa = 0.4) and a low agreement between SMMSE and consortium criteria (kappa = 0.07). CONCLUSION: In this pilot study, MoCA appears to be a better screening tool than SMMSE for MCI in the diabetic population.
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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 | 0.005 | 0.016 |
| 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 | 0.000 | 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".