Diffusion Properties of Normal-Appearing White Matter Microstructure and Severity of Motor Impairment in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The effect of white matter hyperintensities as measured by FLAIR MR imaging on functional impairment and recovery after ischemic stroke has been investigated thoroughly. However, there has been growing interest in investigating normal-appearing white matter microstructural integrity following ischemic stroke onset with techniques such as DTI. MATERIALS AND METHODS: Fifty-two patients with acute ischemic stroke and 36 without stroke were evaluated with a DTI and FLAIR imaging protocol and clinically assessed for the severity of motor impairment using the Motricity Index within 72 hours of suspected symptom onset. RESULTS: There were widespread decreases in fractional anisotropy and increases in mean diffusivity and radial diffusivity for the acute stroke group compared with the nonstroke group. There was a significant positive association between fractional anisotropy and motor function and a significant negative association between mean diffusivity/radial diffusivity and motor function. The normal-appearing white matter ROIs that were most sensitive to the Motricity Index were the anterior/posterior limb of the internal capsule in the infarcted hemisphere and the splenium of the corpus callosum, external capsule, posterior limb/retrolenticular part of the internal capsule, superior longitudinal fasciculus, and cingulum (hippocampus) of the intrahemisphere/contralateral hemisphere. CONCLUSIONS: The microstructural integrity of normal-appearing white matter is a significant parameter to identify neural differences not only between those individuals with and without acute ischemic stroke but also correlated with the severity of acute motor impairment.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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 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".