Comparison between the Mini-Mental State Examination and Montreal Cognitive Assessment as a Cognitive Screening Tool in Patients with Human Immunodeficiency Virus-Associated Neurocognitive Disorders
Bibliographic record
Abstract
Abstract INTRODUCTION: The number of human immunodeficiency virus-associated neurocognitive disorders has increased, reaching more than 50% of the cases. However, there are currently no substantial data on the screening methods for this disease. This study aimed to evaluate and compare the Mini-Mental State Examination to the Montreal Cognitive Assessment in human immunodeficiency virus-infected patients. METHODS: This was an observational study comprising 82 human immunodeficiency virus-positive individuals with and without cognitive complaints. RESULTS: Positive correlation (p<0.001) between the Mini-Mental State Examination and the Montreal Cognitive Assessment test scores was observed, but the mean scores revealed that the Mini-Mental State Examination showed worse performance for trails (p<0.001), cube copying (p<0.001), and clock drawing (p<0.001) than the Montreal Cognitive Assessment. CONCLUSIONS: The Mini-Mental State Examination and the Montreal Cognitive Assessment tests should be used concomitantly for the assessment of human immunodeficiency virus-associated neurocognitive disorders, but visuoexecutive and visuospatial dysfunctions are better evaluated using the Montreal Cognitive Assessment test than the Mini-Mental State Examination.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".