Abstract 30: Door-to-puncture and First Pass Times for Endovascular Thrombectomy: Predictors and Changes Over Time in GWTG-stroke
Bibliographic record
Abstract
Background and Purpose: Endovascular thrombectomy (EVT) is now standard of care in eligible patients with acute disabling ischemic stroke, and is more effective when delivered quickly. It is currently unclear whether time targets achieved in clinical trials can also be achieved in routine clinical practice. Here, we describe interval times from stroke onset to patient arrival in emergency room (door) to first pass/initiation of treatment in patients receiving EVT within Get With The Guidelines-Stroke hospitals (GWTG-S). Methods: Data are from sites participating fully as Comprehensive Stroke Centers within GWTG-S from 8/2014-3/2016. Analyzed work flow times include stroke onset to door , door to imaging, imaging to arterial access, arterial access to first pass time (defined as the earliest of either deployment of a mechanical reperfusion device or intra-arterial alteplase initiated) and the composite door-to-first-pass time. Data are described overall and analyzed by calendar year quarters. Trends are tested using the Cochran-Mantel-Haenszel test. Results: Data are reported from 1891 patients from 172 hospitals. Median time from stroke onset to door was 77 mins (IQR 46-144 min), imaging to arterial access was 91 min (64-126 min), and arterial access to first pass time was 18 min (4-31 min) (Figure). Median door-to-first-pass time was 129 min (IQR 97-171 min). Only 5.3% had a door-to-first-pass time <60 minutes while 15.1% achieved this time in <90 minutes. Slight improvements were noted in door-to-first pass (p=0.01) and imaging to arterial access time (p=0.04) by calendar quarter. Provision of IV alteplase was associated with longer imaging-to-arterial access time (median 96 vs. 85 minutes, p<0.001). Conclusion: Although workflow is improving, efforts need to continue on streamlining workflow and saving time so that the true potential of EVT is realized. These data may inform benchmark goals for EVT workflow times.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.005 | 0.001 |
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".