Workflow and Outcome of Thrombectomy in Late Time Window: A Pooled Multicenter Analysis
Bibliographic record
Abstract
BACKGROUND: We investigated the impact of workflow times on the outcomes of patients treated with endovascular thrombectomy (EVT) in the late time window. METHODS: Individual patients' data who underwent EVT in the late time window (onset to imaging >6 hours) were pooled from seven registries and randomized clinical trials. Multiple time intervals were analyzed. Mixed-effects logistic regression was used to estimate the likelihood of functional independence at 90 days (modified Rankin Scale 0-2). Mixed-effects negative binomial regression was used to evaluate the relationship between patient characteristics and workflow time intervals. RESULTS: 608 patients were included. The median age was 70 years (IQR: 58-71), 307 (50.5%) were female, and 310 (53.2%) had wake-up strokes. Successful reperfusion was achieved in 493 (81.2%) patients, and 262 (44.9%) achieved 90-day mRS 0-2. The estimated odds of functional independence decreased by 13% for every 30 minute delay from emergency department (ED) arrival to imaging time and by 7% from ED arrival to the end of EVT in the entire cohort. Also, the estimated odds of functional independence decreased by 33% for every 30 minute delay in the interval from arterial puncture to end of EVT, 16% in the interval from arrival in ED to end of EVT and 6% in the interval from stroke onset to end of EVT among patients who had a wake-up stroke. CONCLUSION: Faster workflow from ED arrival to end of EVT is associated with improved functional independence among stroke patients treated in the late window.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".