Do Physicians Intuitively Select Slow Progressors for Thrombectomy in the Extended Time Window?
Bibliographic record
Abstract
BACKGROUND: In acute ischemic stroke, a longer time from onset to endovascular treatment (EVT) is associated with worse clinical outcome. We investigated the association of clinical outcome with time from last known well to arrival at the EVT hospital and time from hospital arrival to arterial access for anterior circulation large vessel occlusion patients treated > 6 hours from last known well. METHODS: Retrospective analysis of the prospective, multicenter cohort study ESCAPE-LATE. Patients presenting > 6 hours after last known well with anterior circulation large vessel occlusion undergoing EVT were included. The primary outcome was the modified Rankin Scale (mRS) score at 90 days. Secondary outcomes were good (mRS 0-2) and poor clinical outcomes (mRS 5-6) at 90 days, as well as the National Institutes of Health Stroke Scale at 24 hours. Associations of time intervals with outcomes were assessed with univariable and multivariable logistic regression. RESULTS: Two hundred patients were included in the analysis, of whom 85 (43%) were female. 90-day mRS was available for 141 patients. Of the 150 patients, 135 (90%) had moderate-to-good collaterals, and the median Alberta Stroke Program Early CT Score (ASPECTS) was 8 (IQR = 7-10). No association between ordinal mRS and time from last known well to arrival at the EVT hospital (odds ratio [OR] = 1.01, 95% CI = 1.00-1.02) or time from hospital arrival to arterial access (OR = -0.01, 95% CI = -0.02-0.00) was seen in adjusted regression models. CONCLUSION: No relationship was observed between pre-hospital or in-hospital workflow times and clinical outcomes. Baseline ASPECTS and collateral status were favorable in the majority of patients, suggesting that physicians may have chosen to predominantly treat slow progressors in the late time window, in whom prolonged workflow times have less impact on 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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".