Diagnostic Impact of Intracranial Vessel Wall MRI in 205 Patients with Ischemic Stroke or TIA
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Secondary prevention of ischemic stroke depends on determining the cause of the initial ischemic event, but standard investigations often fail to identify a cause or identify multiple potential causes. The purpose of this study was to characterize the impact of intracranial vessel wall MR imaging on the etiologic classification of ischemic stroke. MATERIALS AND METHODS: This was a single-center, retrospective study of 205 consecutive patients who were referred for vessel wall MR imaging to clarify the etiology of an ischemic stroke or TIA. An expert panel classified stroke etiology before and after incorporating vessel wall MR imaging results using a modified Trial of Org 10172 in Acute Stroke Treatment system. We measured the proportion of patients with an altered etiologic classification after vessel wall MR imaging. RESULTS: The median age was 56 years (interquartile range = 44–67 years), and 51% (106/205) of patients were men. Vessel wall MR imaging altered the etiologic classification in 55% (112/205) of patients. The proportion of patients classified as having intracranial arteriopathy not otherwise specified decreased from 31% to 4% (64/205 versus 9/205; P < .001) and the proportion classified as having intracranial atherosclerotic disease increased from 23% to 57% (48/205 versus 116/205; P < .001). Conventional work-up classification as intracranial arteriopathy not otherwise specified was an independent predictor of vessel wall MR imaging impact (OR = 8.9; 95% CI, 3.0–27.2). The time between symptom onset and vessel wall MR imaging was not a predictor of impact. CONCLUSIONS: When vessel wall MR imaging is performed to clarify the etiology of a stroke or TIA, it frequently alters the etiologic classification. This is important because the etiologic classification is the basis for therapeutic decision-making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".