Tmax profile in computed tomography perfusion-based RAPID software maps influences outcome after mechanical thrombectomy in patients with basilar artery occlusion
Bibliographic record
Abstract
BACKGROUND: Computed tomography perfusion (CTP) parameters have been shown to have predictive value for functional outcomes of patients with basilar artery occlusion (BAO). We report the predictive value of CTP-based software (CTP-Rapid Processing of Perfusion and Diffusion (RAPID); iSchemia View) for functional outcomes of patients with BAO after endovascular therapy (EVT). METHODS: Patients with BAO who underwent EVT were retrospectively analyzed in our center from December 2019 to July 2021. Baseline characteristics and imaging parameters from non-contrast CT, CT angiography (CTA), and CTP-RAPID were collected for analysis. RESULTS: Among the 55 patients enrolled in this study, 22 (40.0%) achieved a good functional outcome (modified Rankin Scale score ≤3 at 90 days). In the univariate analysis, posterior circulation Alberta Stroke Program Early CT Score, Basilar Artery on CT Angiography score, posterior circulation CTA score, posterior communicating artery deficiency, perfusion deficit volume in time to maximum (Tmax) >4 s, Tmax >6 s, and mismatch volume were associated with functional outcomes (all p<0.05). In the multivariate analysis, perfusion deficit volume in Tmax >6 s (OR 1.011 (95% CI 1.001 to 1.020)) and posterior circulation CTA score (OR 0.435 (95% CI 0.225 to 0.840)) remained independent outcome predictors (all p<0.05). CONCLUSIONS: Perfusion deficit volume in Tmax >6 s on CTP-RAPID imaging maps and basilar artery on CTA score have potential as functional outcome predictors in patients with BAO after EVT.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 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".