EPA-1593 - Psychometric and clinometric properties of the montreal cognitive assessment (moca) in a greek sample
Bibliographic record
Abstract
Introduction The Montreal Cognitive Assessment(MoCA) is a psychometric tool measuring cognitive function that detects Mild Cognitive Impairment(MGI), a clinical condition which often results in dementia. Objectives To measure the psychometric properties of the assessment. Aims To explore the discriminant validity and internal consistency of the assessment. Methods The study included 132 patients, 56(42.2%)men and 76(57.6%)women. Of them, 12(9.1%) had dementia, 54(40.9%)psychiatric diseases, 7(5.3%)vascular strokes, 3(2.3%)organic psychosyndrome, 17(12.9%) cases were to be investigated and 36(27.3%) were patients without psychiatric illness, who were evaluated under Liaison Psychiatry. The psychometric properties of MoCA were evaluated in comparison to the Mini-Mental-State-Examination(MMSE) and Golden Standard, which was the diagnosis of the treating physician. Statistical analysis was performed with SPSS21. Results The total scale of MoCA had a coefficient alpha of .885 and all the subscales between .878-893. MoCA had a significant high correlation with MMSE(r=-.544 p =.001) and with age(r =-.544 p =.001). No significant differences were found between men and women (t=-.707 p>.05). There was a statistically significant difference between the assessments in moderate and severe cognitive impairment, where MoCA was more sensitive (99%) than MMSE (likelihood ratio=115.3 p =.001) in all diagnostic categories. The specicifity of MoCA was 93% due to fact that it was reduced in patients with organic psychosyndrome when the golden standard found no cognitive impairment. Conclusions MoCA is a brief screening tool of cognitive decline with higher specificity and sensitivity in detection of mild cognitive impairment in patients who scores in normal levels of MMSE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".