Bibliographic record
Abstract
Background: Studies show that diabetes mellitus is the greatest lifestyle risk factor for dementia. Appropriate management and treatment of type 2 diabetes mellitus could prevent the onset and progression of mild cognitive impairment to dementia. MoCA test is high sensitivity with mild dementia but it have not been used and studied widespread in Vietnam. Aim: 1. Using MoCA and MMSE to diagnose dementia in patients with type 2 diabetes mellitus. 2. Assessment of the relationship between dementia and the risk factors. Methods: cross-sectional description in 102 patients with type 2 diabetes mellitus. The Mini-Mental State Examination(MMSE) and the Montreal Cognitive Assessment (MoCA) were used to assess cognitive function. The diagnosis of dementia was made according to Diagnostic and Statistical Manual of Mental Disorders. Results: The average value for MoCA in the group of patients with dementia (15.35 ± 2.69) compared with non-dementia group (20.72 ± 4.53). The sensitivity and specificity of MoCA were 84.8% and 78.3% in identifying individuals with dementia, and MMSE were 78.5% and 82.6%, respectively. Using DSMIV criteria as gold standard we found MoCA and MMSE were more similar for dementia cases (AUC 0.871 and 0.890). The concordance between MoCA and MMSE was moderate (kappa = 0.485). When considering the risk factors, the education,the age, HbA1c, dyslipidemia, Cholesterol total related with dementia in the type 2 diabetes. Conclusion: MoCA scale is a good screening test of dementia in patients with type 2 diabetes mellitus.When compared with the MMSE scale, MoCA scale is more sensitive in detecting dementia. Key words: MoCA, dementia, type 2 diabetes mellitus, risk factors
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".