Abstract WP308: BEMI (Brain Emergency Management Initiative) for Optimizing Hub-EMS-Spoke Transfer Networks
Bibliographic record
Abstract
Objective: To evaluate an expedited “Brain Emergency Management Initiative (BEMI)” transfer process within a telestroke network with the goal of decreasing transfer delays and time to stroke embolectomy intervention. Methods: We conducted an exploratory, retrospective assessment of consecutive acute telestroke patients transferred for potential intervention to compare outcomes in pre-BEMI vs. BEMI periods. Baseline characteristics included age, sex, ethnicity, stroke risk factors, and NIHSS. Times included Spoke In, Spoke Out, Hub In, and groin puncture. Outcomes included discharge destination home, and Symptomatic Intracranial Hemorrhage (SxICH). Results: Overall, 68 transfers were assessed. There were no differences for age, sex, diabetes, hypertension, or atrial fibrillation. There was a higher NIHSS in BEMI (11 pre-BEMI vs. 20 BEMI;p=0.01). There was a shorter spoke door in to door out (143 vs 118 minutes; p=0.01) and spoke door out to hub door in (23min pre-BEMI vs. 21min BEMI;p=0.001). For embolectomy patients, there was a shorter hub door in to reperfusion (83min Pre-BEMI vs. 74min BEMI;p=.04), and rt-PA decision to groin puncture (155min Pre-BEMI vs.130min BEMI;p=.01). There were no sxICH or discharge home differences. Though NIHSS was correlated with home discharge destination (r=-0.26;p=0.04), Hispanic ethnicity (r=0.33;p=0.01), hub door in to reperfusion (r=0.11;p=0.04) and spoke out to reperfusion (r=0.28;p=0.04), no relationship was strong. Conclusions: In our Hub-EMS-Spoke telestroke network, BEMI led to improved evaluation times. Rapid transfer protocols are critical for improving outcomes. BEMI can serve as a model for future rapid stroke transfer pathways. Further work assessing generalizability and outcomes is underway.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".