Large Deep White Matter Lesions May Predict Futile Recanalization in Endovascular Therapy for Acute Ischemic Stroke
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: This study investigated whether large ischemic lesions in the deep white matter (DWM) on pretreatment diffusion-weighted MRI (DWI) predict futile recanalization. METHODS: Consecutive acute stroke patients with anterior circulation ischemia who underwent successful arterial recanalization with thrombolysis in cerebral infarction grade 2b or 3 were enrolled. A large DWI-DWM lesion was defined as a hyperintense lesion in the DWM on initial DWI, located mainly between the anterior and posterior horns of the lateral ventricle. The Alberta Stroke Program Early CT score on CT and DWI and stroke volume on initial DWI were recorded. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) score. Futile recanalization was defined as a 30-day modified Rankin scale score of 3-6 despite successful recanalization. Univariate and multivariate regression analyses were performed to identify predictors of futile recanalization. RESULTS: In 35 of 46 patients (76%) with successful recanalization, futile recanalization was observed in 20 patients (57%). Patients with futile recanalization were older (median age 74 vs. 58 years; p = 0.053), had higher initial NIHSS scores (median 17 vs. 9; p = 0.042), and a higher prevalence of large DWI-DWM lesions (45 vs. 9%; p = 0.022). Logistic regression analysis showed that a large DWI-DWM lesion was an independent predictor of futile recanalization (OR 13.97; 95% CI 1.32-147.73; p = 0.028). CONCLUSION: Patients with large preintervention DWI-DWM lesions may be poor candidates for endovascular therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 it