Collateral state and the effect of endovascular reperfusion therapy on clinical outcome in ischemic stroke patients
Bibliographic record
Abstract
PURPOSE: Clinically successful endovascular therapy (EVT) in ischemic stroke requires reliable noninvasive pretherapeutic selection criteria. We investigated the association of imaging parameters including CT angiographic collaterals and degree of reperfusion with clinical outcome after EVT. METHODS: In our database, we identified 93 patients with large vessel occlusion in the anterior circulation treated with EVT. Besides clinical data, we assessed the baseline Alberta Stroke Program Early CT score (ASPECTS) on noncontrast CT (NCCT) and CT angiography (CTA) source images, collaterals (CT-CS) and clot burden score (CBS) on CTA and the degree of reperfusion after EVT on angiography. Three readers, blinded to clinical information, evaluated the images in consensus. Data-driven multivariable ordinal regression analysis identified predictors of good outcome after 90 days as measured with the modified Rankin Scale. RESULTS: Successful angiographic reperfusion (OR 26.50; 95%-CI 9.33-83.61) and good collaterals (OR 9.69; 95%-CI 2.28-59.27) were independent predictors of favorable outcome along with female sex (OR 0.35; 95%-CI 0.14-0.85), younger age (OR 0.88; 95%-CI 0.83-0.92) and higher NCCT ASPECTS (OR 2.54; 95%-CI 1.01-6.63). Outcome was best in patients with good collaterals and successful reperfusion, but there was no statistical interaction between collaterals and reperfusion. CONCLUSIONS: CTA-collateral status was the strongest pretherapeutic predictor of favorable outcome in ischemic stroke patients treated with EVT. CTA-collaterals are thus well suited for patient selection in EVT. However, the independent effect of reperfusion on outcome tended to be stronger than that of CTA-collaterals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".