3 Medical Imaging for Acute Ischemic and Hemorrhagic Stroke
Bibliographic record
Abstract
This chapter will provide the reader with an overview of the current guidelines on how to best employ medical imaging modalities in acute stroke care. We will include supporting images, clinical pearls, and tables to compare imaging modalities side by side. We will also summarize the latest research considering current and future applications. First, we will review the use of noncontrast head computed tomography (CT), and important signs to look for in both hemorrhagic and ischemic stroke patients. We will introduce the Alberta Stroke Program Early CT Score (ASPECTS) score and its relevance to evaluating the patient with acute ischemic stroke. We will then discuss the utility of CT angiography and CT perfusion studies. We will review the recent studies that have used novel CT perfusion algorithms to extend the window for endovascular thrombectomy procedures in patients with larger vessel occlusions. Acute stroke evaluation with magnetic resonance imaging will be discussed along with a review of how different institutions employ this technique in the acute stroke setting. The merits and detriments of diffusion-weighted imaging and perfusion-weighted imaging will be contrasted. In the final section of the chapter, we will explain the evidence supporting the use of magnetic resonance angiography in specific scenarios. This chapter will outline the utility of CT and MR imaging in determining treatment in the acute period of stroke management. We will cover the key components of current guidelines in order to provider the reader with a succinct summary of both established practice and the latest research.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.046 |
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".