In What Scenarios Does a Mobile Stroke Unit Predict Better Patient Outcomes?
Bibliographic record
Abstract
Background and Purpose- The mobile stroke unit (MSU) brings imaging and thrombolysis to patients in the field. The MSU has the potential to decrease time from onset to thrombolysis; however, this depends on the location of the patient, the MSU, and the hospital. The MSU will only be able to treat a small subset of patients it is dispatched to. Using conditional probability modeling, we evaluate in which scenarios the MSU exhibits clear benefit over the direct-to-mothership method. Methods- Previously published conditional probability models for drip-and-ship versus mothership transport were modified to reflect MSU workflow. It was assumed that the MSU was dispatched from the endovascular therapy center. Eight scenarios were generated, varying treatment efficiency on the MSU and at the endovascular therapy center and the threshold for dispatching the MSU (low threshold: low treatment rate but few missed patients; high threshold: higher treatment rate, potential for missed treatment opportunities). Results- The relative difference in outcomes between the MSU and mothership was small. Geographic areas where the MSU is superior to mothership increase in size as treatment time on the MSU decreases. When a high-threshold dispatch system is used, the area where the MSU is superior decreases, but the relative difference in predicted outcomes between the MSU and mothership increases. The largest relative difference favoring the MSU was found in areas where the patient would forgo access to alteplase, based upon a 4.5-hour treatment threshold, using mothership transport. Conclusions- There are few scenarios where MSU transport predicts substantially superior outcomes to the mothership method when the MSU is dispatched from the endovascular therapy center. Outcomes using the MSU are maximized when dispatch criteria that maximize patients eligible for thrombolysis treatment are used and treatment times on the MSU are short relative to those of the endovascular therapy center.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".