Is the Montreal Cognitive Assessment (MOCA) test better suited to cognitive impairment detection among Latino people than the Mini-Mental State Examination (MMSE)
Bibliographic record
Abstract
In a sample of 60 patients over the age of sixty and Spanish as mother-tongue, the Montreal Cognitive Assessment (MOCA) and the Mini-Mental State Examination (MMSE) tests were carried out to determine if they can be used equally in patients with cerebrovascular disease of small vessel and clinically perceptible affectations of cognitive impairment and Dementia; and obtain similarly valid results. The population with Dementia and cognitive impairment is increasing. Multiple tools and techniques have been perfected to study this health condition to measure mental problems and Dementia. To obtain the sample, we used the simple random method. A protocol of 30 questions focused on evaluating complex cognitive functions was used to apply the MOCA test. In the MMSE test, an 11-question protocol was used to evaluate essential cognitive functions. The results showed that the MOCA test correctly identified an actual positive rate of 89.6% and a true negative rate of 66.7%. The MMSE test had a false positive rate of 4.4%, having a higher probability of falsely identifying an individual with cognitive impairment. The tests help determine the degree of cognitive deterioration, but with different sensitivities according to their level of studies, which should be preferred over the MOCA. Keywords: Mental health; MMSE; MOCA; cognitive impairment; elderly
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".