Susceptibility-diffusion mismatch correlated with leptomeningeal collateralization in large vessel occlusion stroke
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between asymmetric prominent hypointense vessels (prominent vessel sign, PVS) on susceptibility-weighted imaging (SWI) and leptomeningeal collateralization in patients with acute ischemic stroke due to large vessel occlusion. METHODS: We retrospectively enrolled patients with M1 segment occlusion of the middle cerebral artery who underwent emergency magnetic resonance imaging and digital subtraction angiography within 24 hours from stroke onset. The extent of PVS on SWI was assessed using the Alberta Stroke Program Early CT Score (ASPECTS). Leptomeningeal collateralization on digital subtraction angiography images was assessed using the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) scale. Spearman's rank correlation test was performed to explore the correlation of ASITN/SIR scores with SWI-ASPECTS and SWI-diffusion-weighted imaging (DWI) mismatch scores. RESULTS: Thirty-five patients were enrolled. There was no significant correlation between SWI-ASPECTS and ASITN/SIR scores. However, SWI-DWI mismatch scores were positively correlated with ASITN/SIR scores. CONCLUSION: The range of PVS on SWI did not closely reflect the collateral status, while the range of SWI-DWI mismatch was significantly correlated with the leptomeningeal collateralization. In patients with acute anterior circulation stroke due to large vessel occlusion, larger SWI-DWI mismatch was associated with better leptomeningeal collaterals.
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".