Evaluation of a Practical Visual MRI Rating Scale of Brain White Matter Hyperintensities for Clinicians Based on Largest Lesion Size Regardless of Location
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Age-related white matter hyperintensities have prognostic implications, but no accepted clinical standard exists for their assessment. We propose a simple objective visual rating system by using 3T brain MR imaging. MATERIALS AND METHODS: MR imaging from 559 participants was processed by using an automated method to determine WMH volumes and evaluated with a new visual rating scale based on the single largest WMH lesion diameter regardless of location. The reproducibility of the visual system was assessed. The association of WMH visual scores and automated volumes was then compared with cognitive scores from the Montreal Cognitive Assessment, which was available for 510 participants. RESULTS: Inter-reader reproducibility was good for subsamples with both high (n=52) and low (n=40) prevalence of large automated WMH volumes (agreement of 67% and 87.5%, κ=0.71 and 0.76, respectively). Correlation between increased WMH and cognitive deficit measurements was equal for our visual ratings and automated volumes (Spearman ρ=0.118 and 0.109; P values=0.008 and 0.014, respectively). The visual scale retained a significant association with MoCA score after adjusting for age, sex, and education (standardized β=-0.087, P=.042). CONCLUSIONS: We propose a simple visual WMH scoring system suitable for use as a baseline evaluation in clinical practice.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".