From “Time is Brain” to “Imaging is Brain”: A Paradigm Shift in the Management of Acute Ischemic Stroke
Bibliographic record
Abstract
Arterial recanalization to restore the blood supply and limit the brain damage is the primary goal in the management of acute ischemic stroke (AIS). Since the publication of pivotal randomized clinical trials in 2015, endovascular thrombectomy has become part of the standard of care in selected cases of AIS from large-vessel occlusions up to 6 hours after the onset of symptoms. However, the association between endovascular reperfusion and improved functional outcome is not strictly time dependent. Rather than on rigid time windows, candidates should be selected based on vascular and physiologic information. This approach places imaging data at the center of treatment decisions. Advances in imaging-based management of AIS provide crucial information about vessel occlusion, infarct core, ischemic penumbra, and degree of collaterals. This information is invaluable in identifying patients who are likely to benefit from reperfusion therapies and excluding those who are unlikely to benefit or are at risk of adverse effects. The approach to reperfusion therapies continues to evolve, and imaging is acquiring a greater role in the diagnostic work-up and treatment decisions as shown in recent clinical trials with extended time window. The 2018 American Heart Association/American Stroke Association guidelines reflect a paradigm shift in the management of AIS from "Time is Brain" to "Imaging is Brain." This review discusses the essential role of multimodal imaging developing from recent trials on therapy for AIS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".