Effect of Baseline CT Scan Appearance and Time to Recanalization on Clinical Outcomes in Endovascular Thrombectomy of Acute Ischemic Strokes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: the Penumbra Pivotal Stroke Trial reported a 25% good outcome (modified Rankin scale score ≤ 2) despite an 81% recanalization rate. We evaluated the association of a favorable initial noncontrast CT and a short time to recanalization in predicting good outcome. METHODS: data were from the Penumbra Pivotal Stroke Trial. Baseline scans were evaluated by 2 experienced readers blinded to outcomes using ASPECTS. ASPECTS scores were dichotomized into >7 and ≤ 7 for primary analysis. Data on degree of recanalization based on thrombolysis in myocardial infarction scores, stroke onset to recanalization, and CT to recanalization times were obtained. Primary outcome was modified Rankin scale score ≤ 2 at 3 months. RESULTS: median baseline NIHSS was 18 (range, 8-34) and median baseline ASPECTS score was 6 (range, 0-10); 81.2% achieved recanalization (thrombolysis in myocardial infarction, 2-3) and (27.7%) achieved good outcome. Good outcome was significantly higher in the ASPECTS score >7 group when compared to the ASPECTS score ≤ 7 group (50% vs 15%; RR, 3.3; 95% CI, 1.6-6.8; P=0.0001). No patient with an ASPECTS score ≤ 4 (n=28) or without recanalization (n=16) had a good outcome. There was an interaction between baseline ASPECTS score (>7 and ≤ 7) and onset to recanalization time (≤ 300 minutes and >300 minutes) in predicting good outcome (P=0.06). CONCLUSIONS: patients with baseline CT ASPECTS score ≤ 4 do not benefit from recanalization. Fast recanalization may benefit patients with evident damage on the CT scan (ASPECTS score >4). Overall, patients benefit the most with early recanalization and a favorable baseline CT scan (ASPECTS score >7).
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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 | 0.006 |
| 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 | 0.001 |
| 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".