Imaging-based Selection for Endovascular Treatment in Stroke
Bibliographic record
Abstract
Treatment of acute ischemic stroke (AIS) has evolved significantly in the past few years. Endovascular treatment (EVT) is now proved to be efficacious up to 24 hours from onset in properly selected patients. The recently updated 2018 American Heart Association–American Stroke Association guidelines reflect the important role of imaging in triage and patient selection for EVT of AIS. Pretreatment imaging in patients with acute stroke should (a) allow assessment for intracranial hemorrhage and demonstrate (b) the extent of early ischemic changes, (c) the presence of large arterial occlusion, and (d) in some cases potential salvageable tissue before the decision to proceed with EVT. The authors review how multimodality imaging can be used for EVT selection in the context of the recent guidelines. They highlight the importance of having streamlined imaging workflows that are integrated with clinical decision making to maximize treatment efficiency. Knowledge of the various imaging criteria including perfusion imaging used for EVT selection is highlighted. The authors discuss variable imaging paradigms used for selection of patients in the early and late windows (who present before vs after 6 hours from onset of symptoms), as reflected in the latest guidelines and in relation to their level of evidence. Finally, they focus on challenges in the subgroups of patients who were excluded from recent EVT trials and with limited evidence to prove the efficacy of EVT, such as patients with low NIHSS (National Institutes of Health Stroke Scale) score, distal occlusion, or large ischemic core. ©RSNA, 2019 See discussion on this article by Leslie-Mazwi.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".