How to differentiate intracranial atherosclerotic disease or vasospasms after mechanical thrombectomy. Be patient or vasodilator is the secret?
Bibliographic record
Abstract
Here we describe a successful mechanical thrombectomy (MT) for acute large vessel occlusion in stroke treatment with one passage (thrombolysis in cerebral infarction, TICI 3). Immediately after the withdrawing of the stent retriever, a narrowing of the middle cerebral artery was diagnosed. The rate of vasospasms during this procedure can be as higher as 41% (range from 6-41%). Here we describe our protocol when a narrowing of the artery is visualized after a stent retriever is withdrawn. A patient presented in our emergency room with National Institute of Health Stroke Scale (NIHSS) of 21, Alberta Stroke Program Early CT Score (ASPECTS) 8, computed tomography angiography revealed occlusion of the M1 segment and MT was indicated. One passage TICI Ⅲ was achieved. After that, the image showed a narrowing of the artery. We present one case of a spasm after stent retriever technique for MT, we injected vasodilator and the artery became normal in a few minutes differentiating between atheromatous stenosis and vasospasm. We present a technical note that can help to make the differentiation of vasospasm or atheromatous disease after MT with the stent retriever technique.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".