Modified Visual Magnetic Resonance Rating Scale for Evaluation of Patients with Forgetfulness
Bibliographic record
Abstract
BACKGROUND: As cognitive impairment increases with age, sulcal atrophy (SA) and the enlargement of the ventricles also increase. Considering the measurements on the previously proposed visual scales, a new scale is proposed in this study that allows us to evaluate the atrophy, white matter hyperintensities (WMHs), basal ganglia infarct (BGI), and infratentorial infarct (ITI) together. Our aim of this study is to propose a practical and standardized MRI for the clinicians to be used in daily practice. METHODS: A total of 97 patients older than 60 years and diagnosed with depression or Alzheimer's disease (AD) are included. Cranial MRI, Mini Mental State Examination (MMSE), detailed neuropsychometric tests, and depression scales are applied to all patients. The SA, ventricular atrophy (VA), medial temporal lobe atrophy (MTA), periventricular WMH (PWMH), subcortical WMH (SCWMH), BGI, and ITI are scored according to the scale. The total score is also recorded. RESULTS: The average age of the patients was 74.53, and the mean MMSE score was 22.7 in the degenerative group and 27.8 in the non-degenerative group. Among the patients, 50 were diagnosed with AD. All parameters significantly increased with age. In the degenerative group, SA, VA, MTA, PWMH, SCWMH, and total scores were found to be significantly higher. Sensitivities of VA, PWMH, SCWMH, and total scores, as well as both sensitivity and specificities of MTA score, were observed to be high. When they were combined, sensitivities and specificities were found to be high. CONCLUSION: The scale is observed to be predictive in discriminating degenerative and non-degenerative processes. This discrimination is important, particularly in depressive patients complaining of forgetfulness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".