Time for a Time Window Extension: Insights from Late Presenters in the ESCAPE Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The safety and efficacy of endovascular therapy for large-artery stroke in the extended time window is not yet well-established. We performed a subgroup analysis on subjects enrolled within an extended time window in the Endovascular Treatment for Small Core and Proximal Occlusion Ischemic Stroke (ESCAPE) trial. MATERIALS AND METHODS: Fifty-nine of 315 subjects (33 in the intervention group and 26 in the control group) were randomized in the ESCAPE trial between 5.5 and 12 hours after last seen healthy (likely to have groin puncture administered 6 hours after that). Treatment effect sizes for all relevant outcomes (90-day mRS shift, mRS 0–2, mRS 0–1, and 24-hour NIHSS scores and intracerebral hemorrhage) were reported using unadjusted and adjusted analyses. RESULTS: There was no evidence of treatment heterogeneity between subjects in the early and late windows. Treatment effect favoring intervention was seen across all clinical outcomes in the extended time window (absolute risk difference of 19.3% for mRS 0–2 at 90 days). There were more asymptomatic intracerebral hemorrhage events within the intervention arm (48.5% versus 11.5%, P = .004) but no difference in symptomatic intracerebral hemorrhage. CONCLUSIONS: Patients with an extended time window could potentially benefit from endovascular treatment. Ongoing randomized controlled trials using imaging to identify late presenters with favorable brain physiology will help cement the paradigm of using time windows to select the population for acute imaging and imaging to select individual patients for therapy.
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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 | 0.007 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".