Shedding Light on Mobile Stroke Unit Dispatch Protocols
Bibliographic record
Abstract
Background: Treatment on Mobile Stroke Units (MSU) improves outcome for patients with acute ischemic stroke, however MSU effectiveness relies on accuracy of field dispatch. We aimed at collecting representative data on dispatch infrastructure, methods of stroke identification at the dispatcher level, operation rules and accuracy of dispatcher impression relevant to MSU operations worldwide. Methods: A survey of the PREhospital Stroke Treatment Organization (PRESTO) was conducted in 2020 to include all operational MSU clinical services worldwide. Twenty of 23 MSU services (87%) on four continents responded and participated. We assessed modes of dispatch, level of dispatcher training, numbers of and reasons for dispatches, frequency of MSU cancellation before arrival at scene and diagnoses of patients with MSU management. Results: All 20 participating MSUs reported dispatching from EMS dispatch centers. Fourteen sites also reported responding to alerts from EMS following patient evaluation. With the exception of 2 MSUs, all reported initial dispatcher training for stroke recognition, but only 6 (30%) performed regular training. Median number of dispatches per year was 325 ranging from 119 to 2174. In addition to dispatches for suspected stroke, 8 (40%) were dispatched to cardiac arrest and 13 (65%) for altered level of consciousness runs. One MSU responded to other dispatch call types including seizure, syncope, headache, sick person, and other, if the call information yielded a suspicion of possible stroke diagnosis. A median of 41% of deployments were cancelled en route. Stroke was excluded in 48% of patients assessed on scene. Eighteen percent of assessed patients were diagnosed with cerebral ischemia within 4.5 hours. Conclusions: Allocating specialized resources such as MSUs to the most clinically appropriate calls is key to their efficacy and their ability to result in improved patient outcomes. Improving dispatcher recognition of stroke can potentially be ameliorated by local team education and routine feedback.
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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 | 0.085 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".