Grading and Interpretation of Cerebral White Matter Hyperintensities Using Statistical Anatomic Maps Adjusted for Age and Hypertension
Bibliographic record
Abstract
Introduction: White matter hyperintensities (WMHs) are frequently observed on magnetic resonance images (MRIs) of elderly people, with the prevalence and severity substantially varying among individuals. Clinically however, there have been no rigorous graphical and statistical reference data available for personalized assessment of the relative severity of WMH burden. Methods: In this prospective study of 2,699 first-ever ischemic stroke patients enrolled consecutively from 11 stroke centers in Korea, we generated a clinically useful WMH-grading system based on a large reference data library of age/hypertension-stratified topographic frequency-volume maps, and deciphered WMH variability by investigating the impact of major (age/hypertension) and other minor risk factors on WMH volumes. To this end, we quantitatively registered WMHs on fluid-attenuated inversion-recovery MRIs onto the Montreal Neurologic Institute (MNI) template using a custom-built software package. Results: WMH volumes ranged from 0 to 9·37% (median = 0·61%) of whole brain volume. WMH volume on each percentile map gradually increased with increasing age. For all patients (median age = 69), multivariable analysis showed that age, hypertension, atrial fibrillation, and left ventricular hypertrophy (LVH) were independently associated with increasing WMH. For younger (≤ 69) hypertensives (n = 819), age and LVH were positively associated with WMH volume. For older (≥ 70) hypertensives (n = 944), age and cholesterol had positive relationships with WMH, whereas diabetes, hyperlipidemia, and atrial fibrillation had negative relationships with WMH. For younger non-hypertensives (n = 578), age and diabetes were positively related to WMH. For older non-hypertensives (n = 328), only age was positively associated with WMH. Conclusion: We generated a new visual WMH-grading system, correlated to vascular risk factors and adjusted for age/hypertension. The reference dataset enables to estimate age/hypertension-adjusted percentile ranks of WMH volume in individual patients, allowing a tailored patient-specific interpretation 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".