Mapping the ischemic penumbra and predicting stroke progression in acute ischemic stroke: the overlooked role of susceptibility weighted imaging
Bibliographic record
Abstract
Abstract Objectives Asymmetrically prominent veins (APVs) detected on susceptibility weighted imaging (SWI) in acute stroke patients are assumed to signify compromised cerebral perfusion. We aimed to explore the role of APVs in identifying the ischemic penumbra and predicting stroke progression in acute stroke patients Methods Twenty patients with a middle cerebral artery ischemic infarction presenting within 24 h of symptoms onset underwent SWI following our standard MR stroke protocol imaging sequences which included diffusion-weighted imaging (DWI). Follow-up (FUP) FLAIR images were obtained at least 5 days after the initial MRI study. The Alberta Stroke Program Early CT Score (ASPECTS) was used to determine the initial infarct size, extent of APVs and final infarct size on initial DWI, SWI, and FUP images respectively. For each patient, SWI was compared with DWI images to determine match/mismatch of their respective ASPECTS values and calculate mismatch scores, whereas acute DWI findings were compared with follow-up images to identify infarct growth (IG) and calculate infarction growth scores (IGS). Results IG occurred in 6/10 patients with a positive DWI-SWI mismatch and in none of the patients without a positive DWI-SWI mismatch. A positive DWI/SWI mismatch was significantly associated with IG (χ2 = 8.57, p = 0.0138, Cramer’s V = 0.65). A significant inverse correlation was found between SWI ASPECTS and IGS (rs = − 0.702, p = 0.001). DWI-SWI mismatch scores were strongly correlated with IGS. (rs = 0.788, p = 0.000) Conclusion A positive DWI-SWI mismatch is an indicator of the ischemic penumbra and a predictor of infarct expansion if left untreated.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".