Acute ischemic stroke with tandem/terminal ICA occlusion - CT perfusion based case selection for mechanical recanalization
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Bibliographic record
Abstract
BACKGROUND: Rapid reperfusion in a patient with a favorable penumbral pattern is crucial to achieving a good outcome in acute ischemic stroke. Recanalization rates for tandem and terminal internal carotid artery (ICA) occlusion are better with endovascular management as compared with intravenous tissue plasminogen activator (IV-tPA) alone. We hypothesize that tissue-based selection would enable the identification of the ideal patient most suited for reperfusion therapy. We present our series of patients who developed tandem or terminal ICA occlusion and were selected for endovascular management based on their computed tomography (CT) perfusion (CTP) imaging. RESULTS: In this prospective study, 14 (29.16%) of the 48 patients treated by endovascular intervention between January 2011 and March 2014 had either tandem or terminal ICA occlusion. In the tandem group, thrombolysis in cerebral infarction (TICI) 2b/3 reperfusion and a good outcome was observed in five (71.42%, n = 7) and six patients (85.71%, n = 6), respectively. Among the terminal ICA occlusion group, TICI 2b/3 reperfusion and a good outcome was observed in three (42.8%, n = 7) and two patients (28.5%, n = 7), respectively. In patients with early reperfusion, a strong correlation with a median difference of one, in cerebral blood volume (CBV) Alberta Stroke Program Early CT Score (ASPECTS) on CBV map and post-procedure 24-h non-contrast CT, was noted. The median imaging-to-puncture and puncture-to -meaningful reperfusion time was 70 and 68.5 min, respectively, and, overall, good outcomes were seen in 57.1% of the patients. CONCLUSION: The cerebral blood volume (CBV) core estimation reliably predicted the final infarct volume. The key reasons for the significantly better outcomes seen in our cohort were the stringent perfusion imaging-based patient selection and the rapid reperfusion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it