Value of brain computed tomographic angiography to predict post thrombectomy final infarct size and clinical outcome in acute ischemic stroke
Bibliographic record
Abstract
Aims: This study aims to analyze the predictor in preoperative brain computed tomographic angiography (CTA) for final infarct and outcome in postendovascular thrombectomy patient. Subjects and Methods: 52 patients were retrospectively reviewed. The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) comparison between preoperative noncontrast computed tomography (NCCT) and 24-h NCCT as well as preoperative CTA source image (CTA-SI) and 24-h NCCT were performed. Factors associated with increased ASPECTS and clinical outcome were evaluated. Results: Preoperative NCCT ASPECTS = 24-h NCCT in 23%. Whereas, 46% showed preoperative CTA-SI ASPECTS = 24-h NCCT. Moreover, 40.4% showed 24-h NCCT ASPECTS > preoperative CTA-SI (increased ASPECTS). The two significant factors associated with increased ASPECTS are thrombolysis in cerebral infarct score 2b/3 (P = 0.02) and good collateral status (P = 0.02). Finally, good clinical outcome was associated with age <60 (P = 0.04), preoperative CTA-SI ASPECTS >5 (P = 0.01), good collaterals status (P = 0.02), and increased ASPECTS (P = 0.05). Conclusions: Preoperative brain CTA provided the necessary factors that are associated with good clinical outcomes, which are CTA-SI ASPECTS > 5, good collateral status, and increased ASPECTS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".