Correlation of magnetic resonance imaging parameters with montreal cognitive assessment-Indonesian (MOCA-INA) score in dementia patients at Prof. Dr. IGNG Ngoerah General Hospital, Denpasar, Bali, Indonesia
Bibliographic record
Abstract
Abstract Background Dementia results from various diseases and injuries affecting the brain, with Alzheimer’s disease being the most common form, contributing to 60–70% of cases. Diagnosing dementia and assessing the severity of cognitive symptoms often involve various evaluation tools. One commonly used tool is the Montreal Cognitive Assessment—Indonesian (MOCA-INA), which is specifically designed to evaluate cognitive function across multiple domains. Methods MRI results of 28 dementia patients who have undergone MOCA-INA scoring at RSUP Prof. Dr. I.G.N.G. Ngoerah General Hospital, Denpasar, from January 2022 to December 2023, were selected. MRI parameters and MOCA-INA score were assessed using the Spearman correlation test. Correlation between variables was evaluated based on the correlation coefficient, which indicates the direction and strength of the correlation. Results In this study, no statistical differences were found between multiple MRI parameters and MOCA-INA scores. However, weak inverse relationships were noted for the GCA and MTA scale, and the remaining Kolam and Fazekas score was found to have a positive relationship with the MOCA-INA score. Conclusion Parameters such as clinical status, GCA scale, MTA scale, Fazekas scale, strategic infarction, Koedam score, stroke, hypertension, diabetes mellitus, dyslipidemia, and cardiovascular disease do not have significant correlations with MoCA-INA scores. This may be caused by a relatively small sample size and further study with a broader and larger sample size may help in overcoming the issue.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".