Abstract WP370: DESTINE: Interactive Software Modeling Best Transport Option for Ischemic Stroke Patients to Access EVT Using a Novel 2-Dimensional Temporal Spatial Visualization
Bibliographic record
Abstract
Introduction: There is uncertainty regarding the best transport strategy for ischemic stroke patients with a suspected large vessel occlusion since endovascular therapy (EVT) has become standard of care. Patients can be transported directly to an EVT hospital (mothership), or first to a closer hospital for alteplase and then transferred to the EVT hospital (drip-and-ship). Based on mathematical models, we have developed DESTINE (DEcision Support Tool IN Endovascular therapy), a cloud-based interactive software, which produces visualizations depicting whether drip-and-ship or mothership results in the greatest probability of good outcome. The results are customizable to each system (travel time between treatment centres, treatment efficiency, field population). A novel visualization technique (2-D temporal-spatial visualization) is used to display the model results to the user (Figure). Methods: A usability study was performed with a group of healthcare administrators and clinicians. Users were introduced to the software and asked to perform several tasks and then interpret the results. Users were also asked open-ended questions to help better understand their experience with using a 2-D temporal-spatial diagram. Sessions were screen and audio recorded, audio was transcribed verbatim and analyzed using inductive thematic analysis. Results: 67% of participants were physicians and 33% paramedics, 67% were female and the average age was 40.0 years (SD: 10.54). Some users remarked that once familiarized with the software it was simple to use and the visualizations was clear. Although others felt a video tutorial or reference image would have been helpful. Users also thought the ability to compare two visualizations was beneficial. Several suggestions for improvements were also made. Conclusion: The study results have illuminated that 2-D temporal-spatial visualizations can be used to display the results of a stroke transportation model to end users.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".