Abstract TMP82: When Does Having a Mobile Stroke Unit Make Sense in a Metropolitan Area From a Patient Outcome Perspective?
Bibliographic record
Abstract
Introduction: The Mobile Stroke Unit (MSU) has brought stroke imaging and alteplase administration to the field, possibly shortening onset to needle time in some situations. However, for patients needing endovascular therapy (EVT) onset to groin puncture times may be lengthened due to additional travel time. For patients with suspected large vessel occlusion (LVO) using the Los Angeles Motor Scale (LAMS) we compare the probability of good outcome for patients taken direct to Comprehensive Stroke Centre (CSC) (mothership) and patients utilizing the MSU. Methods: Using conditional probability models for patients with suspected LVO (LAMS ≥4) the probability of good outcome for the mothership and MSU scenarios were generated. Good outcome was defined using a sliding dichotomy, where for LVO patients it was defined as mRS 0-2 as 90 days, and for non-LVO patients it was defined as mRS 0-1. Data from the HERMES collaboration was used for EVT patients, data from the Emberson meta-analysis (extrapolated to the HERMES population for LVO) was used for alteplase treated patients. Probability of good outcome for intracranial hemorrhage and stroke mimics was considered time invariant. Results: The results are visualized in a small metropolitan area with one CSC and one MSU which is housed at the CSC. In the area immediately surrounding the CSC both the MSU and mothership methods predict equivalent outcomes, however moving further away from the CSC mothership predicts better outcomes due to the additional travel time for the MSU. If the MSU does not lead to time savings when the patient arrives at the CSC this mothership area is expanded. Conclusions: From a patient outcomes perspective in a system where a single MSU is housed at a single CSC there are no area where the MSU shows benefit over the mothership approach. In the area where both models predict equivalent outcomes the individual environment and cost-effectiveness must be taken into context when deciding the best transport strategy.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".