Abstract TP25: Analysis of Workflow and Time to Treatment in the Swift Prime Randomized Controlled Trial
Bibliographic record
Abstract
Background and Purpose: Timely recanalization in large vessel occlusive disease is essential to achieving good outcomes in patients with AIS. As such, the SWIFT PRIME study design incorporated aggressive time metrics and real time direct feedback. We systemically investigated variables affecting the time spent during discrete patient process steps including patient transport, selection and treatment delivery in patients treated in the SWIFT PRIME trial. Methods: Data was analyzed from the SWIFT PRIME trial, a global, multi-center, prospective, randomized, open, blinded endpoint (PROBE) IDE study comparing functional outcomes in AIS subjects treated with either IV t-PA alone (93 patients) or IV t-PA in combination with Solitaire device (93 patients). Each patient enrollment was analyzed for workflow and direct feedback was provided to the enrolling site. Results: The median time from Emergency Department (ED) arrival to groin puncture was 90 minutes (interquartile range [IQR], 69 - 120) and ED arrival to reperfusion time was 139 minutes (IQR, 108 - 169). The median ED to imaging start time was 16 minutes (IQR, 10-23.5), puncture to device deployment was 24 minutes (IQR, 18-33), and device deployment to reperfusion was 8 minutes (IQR, 5-23). The association between time intervals and baseline characteristics of the patient, mode of arrival at the endovascular-capable hospital and procedural characteristics was evaluated with multivariate negative binomial regression using a logarithmic link function (Figure). Conclusions: Detailed attention to workflow with iterative feedback and aggressive time goals leads to a highly efficient workflow. Future steps for improvement include faster triage and transport of patients to endovascular capable centers as well as advancements in treating patients with difficult anatomical features.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".