The Role of Brain Magnetic Resonance Imaging (MRI) as an Early Detector of Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Along with the increase of the health and prosperity level will affect the life expectancy in Indonesia, there has also been an increase in degenerative disease cases. One of the problems arises is cognitive impairment. The mild version of this impairment is often associated with the increase risk that will eventually lead to dementia. Therefore, early detection of this impairment is necessary. OBJECTIVE: This study is aimed at proving the correlation between Fazekas scale on brain MRI and MoCA-Ina score in defining the degree of cognitive impairment. METHODS: This study employed observational analytic design and cross sectional study for its data collection method. The Fazekas scale on brain MRI of 32 patients was read by 3 radiologist, while the MoCA-Ina scoring was done by a competent neurologist. Both tests were done double blindly. Later on, the correlation between Fazekas scale and MoCA-Ina score would be assessed using Spearman Correlation. RESULTS: ) 0.002, which is smaller than α: 0.05. Therefore, it can be concluded that there is a strong negative correlation between Fazekas scale and MoCA-Ina score. CONCLUSION: Fazekas scale evaluation on brain MRI is necessary to be performed as it helps predicting the decline of one's cognitive function, so that an early therapy can be acted upon to prevent dementia in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".