Improving reperfusion time within the ESCAPE Endovascular Clinical Trial
Bibliographic record
Abstract
INTRODUCTION: Endovascular treatment of acute ischemic stroke is more effective when performed quickly. In this report, we describe quality interventions to ensure fast endovascular treatment times in the ESCAPE (Endovascular Treatment for Small Core and Anterior circulation Proximal Occlusion with Emphasis on Minimizing CT to Recanalization Times) trial. METHODS: An "audit and feedback" intervention using webinar and letter was used to improve treatment time over the course of the trial. The time metrics were computed tomography-to-groin-puncture (target < 60 min) and computed tomography-to-first-reperfusion (target < 90 min). Each site was provided with their data for computed tomography-to-groin-puncture and computed tomography-to-first-reperfusion for all their patients that were randomized to the treatment arm, and their median time was compared to the overall median times of all sites in the trial. We assessed for changes in treatment time over the course of the trial. RESULTS: There were 165 patients enrolled into the endovascular arm from 22 sites. The computed tomography-to-groin-puncture time dropped from 57 to 47 min (p = 0.14) while computed tomography-to-reperfusion time dropped from 89 to 81 min (p = 0.48). Over the course of the trial, the absolute treatment benefit increased by 7.8% (p < 0.001). CONCLUSIONS: An "audit and feedback" intervention throughout the conduct of the ESCAPE trial was a feasible way to ensure fast treatment times. Quality improvement processes should continue as standard practice beyond the trial to encourage good patient selection and the best clinical 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 | 0.054 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".