Abstract TP275: Modelling the Impact of Mobile Stroke Unit Dispatcher Accuracy on Patient Outcomes
Bibliographic record
Abstract
Introduction: Ischemic stroke with large vessel occlusion (LVO) cannot be definitively diagnosed without imaging. The Mobile Stroke Unit (MSU) has brought this capability (along with alteplase administration) to the field. In areas with widespread urban sprawl, having MSUs stationed in the community may improve patient outcomes. However, this is also dependent on how accurately dispatch can assess suspected stroke with LVO over the phone. We compare the probability of good outcome for patients taken direct to Comprehensive Stroke Centre (CSC) (mothership) vs utilizing a MSU at varying levels of dispatcher accuracy in identifying ischemic stroke with suspected LVO. Methods: Conditional probability models for patients with suspected LVO for mothership and MSU scenarios were generated. The positive predictive value (PPV) of dispatcher screening was varied from 25% to 75%. 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 around the CSC with each MSU covering one quarter of the city. If dispatcher accuracy is poor the area where MSU predicts the best outcome is large as more non-LVO strokes, which benefit from fast alteplase, will be picked up. However, as dispatcher accuracy in identifying LVO increases the mothership areas also increase as the MSU would impose delays in these patients receiving EVT. Conclusions: The ability to accurately dispatch the MSU to patients with suspected LVO impacts transport decision making. This should be considered when designing a stroke system containing a MSU.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".