Improvements in endovascular stroke treatment workflow over 5 years: ESCAPE to ESCAPE-NA1
Bibliographic record
Abstract
Background and Purpose Rapid treatment is a major determinant of outcome in acute ischemic stroke patients with large vessel occlusion. We used patient-level data from the ESCAPE and ESCAPE-NA1 trials to evaluate whether and to what extent workflow interval times have improved over time. Methods Data were derived from the ESCAPE and the ESCAPE-NA1 randomized trials. Workflow interval times and reperfusion quality were summarized using descriptive statistics and compared on a patient level between the two trials using the Wilcoxon rank sum test and Fisher's exact test. The effect of patient baseline characteristics, including patient age, sex and stroke severity as measured by the National Institutes of Health Stroke Scale, on workflow times was determined using linear regression. Results All patients from the ESCAPE trial ( n = 315) and the ESCAPE-NA1 trials ( n = 1105) were included in the analysis. For endovascular interval times, control patients from the ESCAPE trial were excluded. All in-hospital workflow interval times, including door-to-reperfusion times, were significantly shorter in ESCAPE-NA1 (median 91 min [IQR 69–120] vs. 110 [IQR 89–143], P < .001). These improvements were mainly observed in patients directly presenting to an EVT-capable hospital. Onset-to-randomization times did not differ significantly between the two trials (ESCAPE-NA1: median 188 [122–319] vs. ESCAPE: 174 [119–285], P = .152). There was no effect of procedural sedation use, age, sex, stroke severity or evidence of a learning effect over the duration of each trial. Conclusion Workflow interval times in endovascular stroke treatment have significantly improved over time, particularly in patients directly presenting to an EVT-capable hospital.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".