Abstract WP42: Prediction of Blood-brain Barrier Disruption and Intracerebral Hemorrhagic Infarction Using Arterial Spin-labeling MRI
Bibliographic record
Abstract
Background and Purpose: Arterial spin-labeling (ASL) MRI is sensitive for detecting hyperemic lesions (HLs) in patients with acute ischemic stroke (AIS). We evaluated whether HLs could predict blood-brain barrier (BBB) disruption and hemorrhagic transformation (HT) in AIS patients. Methods: In a retrospective study, ASL was performed within 6 hours of symptom onset before revascularization treatment in 25 patients with anterior circulation large vessel occlusion on baseline MR angiography. All patients underwent angiographic procedures intended for endovascular therapy and a noncontrast CT scan immediately after treatment. BBB disruption was defined as a hyperdense lesion present on the posttreatment CT scan. A subacute MRI or CT scan was performed during the subacute phase to assess HTs. The relationship between HLs and BBB disruption and HT was examined using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) locations in the symptomatic hemispheres. Results: A HL was defined as a region where CBF relative ≥1.4 (CBF relative =CBF HL /CBF contralateral ). HLs, BBB disruption and HT were found in 9, 15, and 15 patients, respectively. Compared with the patients without HLs, the patients with HLs had a higher incidence of both BBB disruption (100% versus 37.5%, P=0.003) and HT (100% versus 37.5%, P=0.003). Based on the ASPECTS locations, 21 regions of interests (ROIs) displayed HLs. Compared with the ROIs without HLs, the ROIs with HLs had a higher incidence of both BBB disruption (42.8% versus 3.9%, P<0.001) and HT (85.7% versus 7.8%, P<0.001). Conclusion: HLs detected on pretreatment ASL maps may enable the prediction and localization of subsequent BBB disruption and HT.
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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) | 0.000 | 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".