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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".