Abstract WP279: Modelling the Impact of Multiple Mobile Stroke Units Surrounding a Metropolitan Area
Bibliographic record
Abstract
Introduction: The Mobile Stroke Unit (MSU) has brought stroke imaging and alteplase administration to the field, shortening onset to needle time in some situations. In areas with widespread urban sprawl, having MSUs stationed in the community may improve patient outcomes. 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) vs. utilizing multiple MSUs. Methods: Using conditional probability models for patients with suspected stroke with LVO (LAMS ≥4) the probability of good outcome for the mothership and MSU scenarios were generated. A sliding dichotomy was used to define good outcome, mRS 0-2 at 90 days was used for LVO patients and mRS 0-1 was used for non-LVO patients. 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 using temporal-spatial diagrams with one CSC and four MSUs stationed at various distances from the CSC with each MSU covering one quarter of the city. The addition of four MSUs in a small geographic area (60 min radius from CSC) does not predict improved patient outcomes. However, as the size of the geographic area increases the relevance of the MSU becomes apparent in the areas closest to the MSU. Conclusions: From a patient outcomes perspective multiple MSUs may be necessary to have the possibility of superior patient outcomes than the mothership transport method if they are situated far away from the CSC. This in addition to the individual environment and cost-effectiveness should be taken into context when deciding the best place to house future MSUs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".