Abstract P354: SWAN-DWI Mismatch Predicts Clinical Outcome After Mechanical Thrombectomy
Bibliographic record
Abstract
Objectives: Asymmetrically prominent veins on magnetic susceptibility sequences are thought to reflect the ischemic penumbra, by detecting high levels of desoxyheglobin. We investigated the relation between star-weighted angiography (SWAN)-diffusion weighted imaging (DWI) mismatch and clinical outcome after mechanical thrombectomy. Methods: We performed a retrospective study on patients who experienced anterior circulation stroke and for whom 1.5-Tesla MRI DWI and SWAN were performed upstream of mechanical thrombectomy. Mismatch was determined by using the Alberta Stroke Program Early CT Score (ASPECTS) on the diffusion-weighted and SWAN sequences. Three subgroups were defined in terms of mismatch level: high mismatch (HM), moderate mismatch (MM) and low mismatch (LM). Favorable outcome was defined as a modified Rankin score of 0-2 at three months. Multivariate logistic regression was used to examine associations between mismatch profiles and favorable outcomes. Results: The study included 108 patients who underwent mechanical thrombectomy. High mismatch was significantly associated with favorable clinical outcome (67% in the HM subgroup vs. 51% and 28% in the MM and LM subgroups respectively; odds ratio 1.25; 95 CI 1.02-1.56; p=0.037). No significant associations between SWAN-DWI mismatch and severe hemorrhagic complications or recanalization quality were brought to light by the present study. Conclusion: SWAN-DWI mismatch is a competent predictor of clinical outcome following mechanical thrombectomy.
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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 | 0.003 |
| 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) | 0.003 | 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 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".