Evaluation of MoCA Scale Ratings with Cognitive Level Correlation in Mild Cognitive Disorders
Bibliographic record
Abstract
Objective: In this study, Montreal Cognitive Assessment (MoCA) test was applied in patients with mild cognitive impairment (MCI). Hippocampal volumes were compared between healthy controls and patients with MCI. Scores of MORBID scales and cognitive level were compared among patients with MCI to investigate if there is a correlation. We aimed to investigate the availability of the test on the diagnosis.Methods: This study included 25 healthy controls and 25 patients diagnosed with MCI according to the Petersen criteria. Detailed neurological examination was performed. MoCA and Standardized Mini Mental State Examination (MMSE) were used in the patient group, whereas MMSE was used in all participants. To all exhibitors cranial magnetic resonance imaging was performed. Right and left hippocampal volumes were calculated using special volumetric three-dimentional T1-weighted inversion recovery sequence. Neuropsychological test results and hippocampal volumes were compared between the patient and control groups.Results: There were significant differences in hippocampal volumes and MMSE scores between the HBB patient and control groups (p<0.001, p=0.011, p=0.014). We found a significant relationship between MoCA average test score and left hippocampal volume with correlation analysis (p=0.013).Conclusion: As a result, hippocampal volume markedly decreased in patients with MCI compared with that in the healthy control group; we found that hippocampal volume reduction is proportional to MoCA scores. These findings suggest that MoCA scores were correlated with cognitive level in MCI and suggests the usefulness of the test in the diagnosis of MCI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".