Impact of Clinical Trial Enrollment on Thrombolysis Workflow in a Mobile Stroke Unit: Results from the AcT Trial
Bibliographic record
Abstract
BACKGROUND: The Edmonton-based mobile stroke unit (MSU), which transports patients to the University of Alberta Hospital (UAH), enrolled patients in the Intravenous Tenecteplase Compared with Alteplase for Acute Ischemic Stroke (AcT) trial. We examined the feasibility of trial enrollment in MSU, its impact on acute stroke workflow metrics and functional outcomes at 90-120 days. METHODS: = 1261). All patients were enrolled with a deferred consent process. The primary outcome for this analysis was the feasibility of enrollment defined as the proportion of patients receiving intravenous thrombolysis (IVT) during the study period who were enrolled in the trial. Multiple linear and binary logistic regression was used to evaluate the adjusted effect of the study groups on acute stroke workflow metrics and functional outcomes at 90-120 days. RESULTS: 100% of eligible IVT-treated patients in the MSU during the study period were enrolled in the AcT trial. Covariate-adjusted linear regression showed shorter door-to-needle (17.2 [9.7-24.6] min) and CT-to-needle (10.7 [4.2-17.1] min) times in the MSU compared to UAH and non-UAH sites. There was no difference in the proportion of patients with an excellent functional outcome (mRS 0-1) at 90-120 days or symptomatic intracerebral hemorrhage (ICH) at 24 hours between groups. CONCLUSIONS: Enrollment in the AcT trial from the MSU was feasible. MSU-enrolled patients demonstrated faster door-to-needle and CT-to-needle times, resulting in earlier IVT administration and similar rates of symptomatic ICH.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".