Efficacy of computed tomography perfusion – Alberta stroke program early computed tomography score for identifying patients with anterior circulation acute ischemic stroke that would benefit from endovascular treatment
Bibliographic record
Abstract
BACKGROUND: The identification of criteria that improves the selection of ischemic stroke patients most suitable for mechanical thrombectomy (MT) will improve clinical outcomes. The aim of this study was to identify the computed tomography (CT) imaging parameter that best predicts patients who will benefit from endovascular treatment among patients with anterior circulation ischemic stroke. MATERIALS AND METHODS: This retrospective study was conducted in patients with acute middle cerebral artery (MCA) stroke with/without internal carotid artery occlusion who underwent successful MT at Siriraj Hospital from November 2009 to October 2016. Evaluated parameters were compared between those with and without a favorable outcome. RESULTS: Forty-four consecutive patients with acute MCA occlusion were included, and 61.4% had unfavorable clinical outcome. Regarding CT perfusion - Alberta stroke program early CT score (CTP-ASPECTS) at the 50% cut point, patients with favorable outcome had higher Cerebral blood volume-ASPECTS (CBV-ASPECTS) and mean transit time-ASPECTS (MTT-ASPECTS) than those with unfavorable outcome. For CTP-ASPECTS at the 75% cut point, patients with favorable outcome had higher CBV-ASPECTS, cerebral blood flow-ASPECTS, and MTT-ASPECTS than those with unfavorable outcome. CONCLUSIONS: CTP-ASPECTS at the 50% and 75% cut points of abnormality could not predict the clinical outcome of anterior ischemic stroke after thrombectomy. Of the ASPECTS evaluated in this study, MTT-ASPECTS at the 75% cut point was the most predictive parameter. Older age was associated with unfavorable outcome after thrombectomy.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".