The Greek version of the Montreal cognitive assessment in coronary artery disease in Cyprus
Bibliographic record
Abstract
Introduction: Mild cognitive impairment (MCI) is defined as a transitional state between normal ageing and \nearly dementia characterized by an increased impairment of cognitive functions for persons of particular age and educational level (without affection of basic activities of daily living), but not meeting the diagnostic criteria for dementia (Gauthier et al. 2006).Aims: To assess the psychometric properties of the Greek version of MOCA (MoCA-Gr) in a Greek-Cypriot population with chronic heart disease (CHD) and the comparison of the tools MoCA-Gr and MMSE on the detection of MCI and the relation of demographic factors with MCI were also investigated. Methods: The study is a methodological survey to validate MoCA-Gr in a specific population. A convenience sample of 150 persons with known CHD were 99 healthy persons (free of heart disease history). MoCA and MMSE questionnaires, along with demographic and clinical information were completed. Content, construct and concurrent validity were assessed. Also, the internal consistency (Cronbach’s alpha) and stability (test-retest)were investigated.Results: The AUC was found to be 0.834 (p<0.001)indicating that the MOCA-Gr can discriminate between CHD patients and healthy group (Picture 1). Cronbach’s a was also found to be good (0.774).Specificity was found to be 58% and sensitivity 93%.MOCA-Gr and MMSE total scores are highly correlated (r=0.766p<0.001) within the sample of 150 CHD patients and also highly correlated (r=0.761 p<0.001) within the total of 249 persons (150 CHD + 99 Healthy control \ngroup).Conclusions: MoCA-Gr may assess mild cognitive impairment among CHD patients with good psychometric properties and be more sensitive than MMSE in detecting MCI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".