Abstract W P28: Diffusion and T2 Star Weighted MR Angiography Mismatch Predicts Ischemic Penumbra in the Acute Stage
Bibliographic record
Abstract
Introduction: T2 star weighted MR angiography (SWAN) can detect slight changes of susceptibility caused by microbleeds. It has also been reported that SWAN can detect hemodynamic insufficiency as hypointensity area in the medullary or cortical veins. In this study, we investigate whether SWAN can help to detect ischemic penumbra in the acute stage. Materials and methods: Patients showing acute major vessel occlusion (internal carotid artery and middle cerebral artery) within 4.5 hours from onset were consecutively analyzed with MR imaging including SWAN, diffusion weighted imaging (DWI) and MR angiography. To evaluate ischemic area in SWAN and DWI, modified Alberta Stroke Program Early CT score (mASPECTS) was used as follows; M1~6 and basal ganglion including caudate, insula, lentiform or internal capsule. SWAN based mASPECTS was calculated with DWI based ASPECTS, and correlation between DWI-SWAN mismatch and final infarct area or outcome was evaluated. Result: Of 30 patients (mean age: 72.7 ± 13.3 years). Cardioembolic stroke was confirmed in 24 patients, atherothrombotic stroke was in 3 patients, and others had unknown etiology. Intravenous t-PA was performed in 17 patients, and endovascular therapy was performed in 17 patients. Overall, recanalization was achieved in 70% (21 patients) with lower modified Rankin Scale at 90 days compared with no recanalization (P=0.0004). Interestingly, SWAN based mASPECTS was significantly correlated with mRS at 90 days (R=-0.4382, P=0.02) regardless of recanalization. Of 9 patients showing no recanalization, DWI-SWAN mismatch was significantly correlated with new infarction (R=0.8221, P=0.0065). On the other hand, patients showing recanalization showed no correlation between mismatch and new infarct. Conclusion: DWI-SWAN mismatch could simply predict ischemic penumbra requiring immediate reperfusion. Assessment of ischemic penumbra from venous side using SWAN is quite useful without contrast media.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.006 | 0.001 |
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".