Visual Alberta stroke program early computed tomography score versus RAPID-AI perfusion in predicting outcome after late-window thrombectomy
Bibliographic record
Abstract
Purpose To evaluate the prognostic utility of visual Alberta stroke program early computed tomography score (ASPECTS) and perfusion parameters obtained from automated RAPID-AI software in patients undergoing mechanical thrombectomy (MT) beyond 6 hours from stroke onset. Methods We retrospectively analyzed 86 patients with anterior circulation large vessel occlusion who underwent non-enhanced computed tomography (NECT), multiphase computed tomography angiography, and computed tomography perfusion within 6–24 hours before thrombectomy. Visual ASPECTS (assessed by junior doctor), RAPID-ASPECTS, and RAPID-CTP parameters (ischemic core volume, penumbra, and mismatch ratio) were recorded. The primary outcome was 90-day functional independence (modified Rankin Score 0-2). Multivariable logistic regression and receiver operating characteristic analysis were used to identify independent predictors. Results Visual ASPECTS was significantly associated with a favorable outcome (area under the curve = 0.709; optimal cut-off ≥ 6), while no perfusion-derived parameters reached statistical significance. In multivariable analysis, only visual ASPECTS (OR 0.083, 95% CI: 0.033–0.133; p = 0.001), hypertension (OR 0.252, 95% CI: 0.053–0.452; p = 0.014), and symptomatic intracranial hemorrhage (OR 0.634, 95% CI: 0.303–0.964; p < 0.001) remained independent predictors. Agreement between visual and RAPID-ASPECTS was moderate (intraclass correlation coefficient 0.67; 95% CI: 0.49–0.80; p < 0.001), but poor when dichotomized at the ≥ 6 threshold (Cohen's kappa κ = 0.18, p < 0.001). Conclusion Visual ASPECTS outperformed perfusion-derived metrics in predicting clinical outcomes after late-window thrombectomy. These findings support the continued relevance of NECT and expert visual scoring, particularly in settings where perfusion imaging may be limited or inconsistent.
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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) | 0.000 | 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".