MoCa predictive power in neuropsychological assessment of patients with dementia
Bibliographic record
Abstract
This study aimed to correlate neuropsicometric tests in elderly over 4 years of schooling and assess MoCA accuracy in diagnosing Alzheimer's disease and mild cognitive impairment (MCI). It evaluated 136 elderly patients treated at the Institute of Geriatrics and Gerontology, from April 2010 to December 2012. The instruments used were the Mini-Mental State Examination (MMSE), Cambridge Cognitive Examination (CAMCOG), Clock Drawing Test (CDT), Verbal Fluency test, Geriatric Depression Scale, Pfeffer Functional Activities Questionnaire (PFAQ), and Montreal Cognitive Assessement (MoCA). ROC curve analysis was used to establish cutoffs and the Pearson correlation coefficient to compare the MoCA with the other tests. The results showed that the MoCA was the best test to differentiate Alzheimer's disease from MCI. The sensitivity and specificity found were 82.2% and 92.3%, respectively. The analysis of the correlation test showed that MoCA is strongly correlated with other tests already validated and wide applied in Brazil. The MoCA test showed the greatest predictive value to differentiate AD from MCI and also differ MCI from normal controls. Furthermore, MoCA was significantly correlated with the age variable and MMSE, CAMCOG, CDT, Verbal Fluency and PFAQ tests, instruments that are already validated and widely used in Brazil.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".