Correlation between quantitative software analysis-based white matter hyperintensity volume on FLAIR image and cognitive impairment
Bibliographic record
Abstract
Motivation: Acquiring new knowledge about the clinical significance of white matter hyperintensity (WMH) is important. Goal(s): This study aimed to investigate the relationship between WMH volume and cognitive impairment. Approach: Patients information (sex, age, education level), neuropsychological examinations (Mini-Mental State Examination and the Japanese version of Montreal Cognitive Assessment), and FLAIR images were retrospectively examined as clinical data. WMH volume was analyzed from FLAIR images with fully automated analysis software. The relationship between WMH volume and clinical data was investigated. Results: WMH volume significantly differed according to education level, and that WMH volume was associated with neuropsychological examinations. Impact: The white matter hyperintensity volume obtained from fluid-attenuated inversion recovery images using a fully automated white matter signal analysis software could provide important clinical information about cognitive impairment in patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".