Relationship between White Matter Hyperintensity Volume Analyzed from Fluid-Attenuated Inversion Recovery Using a Fully Automated Analysis Software and Cognitive Impairment
Bibliographic record
Abstract
INTRODUCTION: White matter hyperintensity (WMH) is associated with cognitive impairment, although the clinical significance of WMH remains unclear. We aimed to elucidate the clinical significance of WMH volume and whether a fully automated quantitative analysis of WMH would be an effective marker of cognitive function. METHODS: Patients with suspected cognitive impairment were retrospectively examined. Clinical data, including patient information, neuropsychological examinations, diagnoses of dementia disorders, and fluid-attenuated inversion recovery (FLAIR) images, were collected. Patient information included sex, age, and educational level. Neuropsychological examinations included the Mini-Mental State Examination (MMSE) and Japanese version of the Montreal Cognitive Assessment (MoCA-J). WMH volumes were analyzed from FLAIR images using a fully automatic analysis software. The relationship between WMH volume and clinical data was investigated. RESULTS: WMH volume was analyzed using 889 FLAIR cases. The WMH volume did not differ significantly between the sexes. WMH volume showed a positive correlation with age. Multiple comparison tests showed no significant difference in WMH volume between junior high school and high school graduates, but all other differences were significant. Multiple comparison tests revealed significant differences in mean WMH volume among all groups in the classified MMSE. The Mann-Whitney U test revealed significant differences in WMH volume between the two groups. Multiple comparison tests revealed significant differences in WMH volume among all the groups of classified diagnostic results. CONCLUSION: Quantitative analysis of WMH volume from FLAIR images may provide useful information for dementia treatment and may be effective as a new marker in cognitive function examinations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".