Abstract 115: CT Perfusion Identifies Patients with Poor Outcomes in The Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion with Emphasis on Minimizing CT to Recanalization Times (ESCAPE) Trial
Bibliographic record
Abstract
Background: In the ESCAPE trial, patients with large vessel occlusions and small infarct cores identified with CT/CT angiography were randomized to endovascular therapy or standard of care. CT Perfusion (CTP) was obtained in some cases, but was not utilized to select patients. We tested the hypothesis that patients with penumbral CTP patterns have higher rates of good clinical outcome. Methods: All CTP data acquired in ESCAPE patients was analyzed centrally using a semi-automated perfusion threshold based approach. A penumbral pattern was defined as an infarct core<70 mL, penumbral volume >15 mL, and a total hypoperfused volume:core volume ratio of >1.8. The primary outcome was a good functional outcome at 90 days (mRS 0-2). Results: CTP was acquired in 138 of 316 ESCAPE patients. Motion degraded CTP images from 9 patients were excluded. Penumbral patterns were present in 110/129 (85.3%) of patients. The rate of good functional outcome in penumbral pattern patients (53/108; 49.1%) was higher than that in non-penumbral patients (3/19; 15.8%, p=0.011). In penumbral patients, endovascular therapy increased the likelihood of good outcome (35/58; 60.3%) over those in the control group (18/50; 36%, OR=2.71, p=0.013). Only 4 of 19 non-penumbral patients were randomized to the endovascular group, preventing an analysis of treatment effect. Conclusion: The majority of patients with CTP imaging in the ESCAPE trial had penumbral patterns, which were associated with better outcomes overall. Patients with penumbra treated with endovascular therapy had the greatest odds of good functional outcome. Non-penumbral patients were much less likely to achieve good outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".