Direct to Angiosuite in Acute Stroke with Mobile Stroke Unit
Bibliographic record
Abstract
ABSTRACT: Background: Early reperfusion has the best likelihood for a favorable outcome in acute ischemic stroke (AIS) with large vessel occlusion (LVO). Our experience with mobile stroke unit (MSU) for direct to angiosuite (DTAS) transfer in AIS patients with suspected LVO is presented. Methods: Retrospective review of prospectively collected data from November 2019 to August 2022, of patients evaluated and transferred by the University of Alberta Hospital MSU and moved to angiosuite for endovascular thrombectomy (EVT). Result: A total of 41 cases were included. Nine were chosen for DTAS and 32 were shifted to angiosuite after stopping for computed tomography (CT) angiography of the head and neck (no-DTAS). Stroke severity measured by NIHSS (median with interquartile range (IQR)) was higher in patients of DTAS, 22 (14–24) vs 14.5 (5–25) in no-DTAS (p = 0.001). The non-contrast CT head in MSU showed hyperdense vessels in 8 (88.88%) DTAS vs 11 (34.35%) no-DTAS patients (p = 0.003). The EVT timelines (median with IQR, 90th percentile) including “door to artery puncture time” were 31 (23–50, 49.2) vs 79 (39–264, 112.8) minutes, and “door to recanalization time” was 69 (49–110, 93.2) vs 105.5 (52–178, 159.5) minutes in DTAS vs no-DTAS group, respectively. The workflow times were significantly shorter in the DTAS group (p < 0.001). Eight (88.88%) out of 9 DTAS patients had LVO and underwent thrombectomy. Conclusions: MSU for DTAS in patients with high NIHSS scores, cortical signs, and CT showing hyperdense vessel is an effective strategy to reduce the EVT workflow time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".