Cognitive Impairment in Patients with Coronary Artery Disease; Comparison of Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE)
Bibliographic record
Abstract
Background & Objective:\n Mild cognitive impairment (MCI) is a transient state between normal \ncondition and dementia. Available data indicates that coronary artery \ndiseases (CAD) may increase the risk of MCI. Hence, the early detection \nof MCI can prevent the progression of cognitive decline. \nMaterials & Methods:\n A sample of 65 subjects with degrees of CAD was enrolled to the study.\n For cognitive assessment, Mini Mental State Examination and Montreal \nCognitive Assessment were used. Sensitivity, specificity, Positive \nPredictive Value (PPV), and Negative Predictive Value (NPV) of MoCA were\n assessed in the cut-off points of 26 and 25. The SPSS 22 was used for \ndata analysis. The statistical significance was set at P-value<0.05. \nResults: The \nprevalence of cognitive impairment was calculated 41.5% and between \n47.7% and 60% by MMSE and MoCA, respectively. At the cut-off point of 25\n for MoCA test, the sensitivity and specificity were 92.6% and 84.2%, \nand PPV and NPV were 80.6% and 94.1%, respectively, and the efficacy of \nMoCA test for the detection of MCI was 87.69%. At the cut-off point of \n26 for MoCA test, the sensitivity and specificity were 96.3% and 65.8%, \nand PPV and NPV were 66.7% and 96.2%, respectively, and the efficacy of \nMoCA test was 78.46%. \nConclusion: The \nprevalence of MCI in patients with CAD was higher than what was \npreviously reported. The MoCA was more sensitive for recognizing the MCI\n in these patients. We suggested the cut-off point of 25 for the higher \naccuracy of the MoCA in detecting MCI in CAD patients.
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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 | 0.001 | 0.003 |
| 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".