Association of White Matter Hyperintensity Measurements on Brain MR Imaging with Cognitive Status, Medial Temporal Atrophy, and Cardiovascular Risk Factors
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: White matter hyperintensities (WMHs) are frequently characterized as markers of cerebrovascular disease, whereas medial temporal atrophy (MTA) is a recognized marker of Alzheimer disease (AD). Our purpose was to test the reliability of a visual rating system (VRS) in evaluating WMHs and MTA and in distinguishing healthy from cognitively impaired subjects. MATERIALS AND METHODS: Subjects (n = 192) enrolled in the Florida Alzheimer's Disease Research Center were diagnosed with no cognitive impairment, nonamnestic mild cognitive impairment (na-MCI), amnestic MCI (a-MCI), or probable AD. The severity of WMHs was assessed on T2-weighted fluid-attenuated inversion recovery axial MR images, and the severity of MTA was evaluated on 1.5-mm-thick coronal MR images by using a computer-based visual rating system. Cardiovascular risk factor scores were calculated as the sum of 10 independent cardiovascular risk factors. RESULTS: WMH and MTA scores were greater in subjects with probable AD, relative to those with no cognitive impairment and na-MCI. MTA scores differentiated subjects with a-MCI from those with no cognitive impairment and na-MCI. The total WMH score was significantly related to MTA (r = 0.39; P < .001) but not to cardiovascular risk factor scores (r = 0.07; P = not significant). The overall correct classification rate of probable AD versus no cognitive impairment by using MTA scores was 81.8%, improving to 86.5% when combined with WMH scores. CONCLUSIONS: Both MTA and WMH scores distinguished subjects with no cognitive impairment and probable AD. Combining MTA and WMH scores improved the correct classification rate, whereas WMH scores were significantly related to MTA scores, but not to cardiovascular risk factor scores. This finding suggests that among subjects with a-MCI and probable AD, WMHs on MR images are primarily associated with neurodegenerative disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".