Abstract Number ‐ 207: Modeling Optimal Patient Transport in a Stroke Network Capable of Remote Telerobotic Endovascular Therapy
Bibliographic record
Abstract
Introduction Endovascular therapy (EVT) is the standard‐of‐care for large vessel occlusion stroke; however, timely access to treatment is still limited for a large proportion of the population. Telerobotic EVT has the potential to decrease time to treatment and expand existing networks of care to more rural populations. It is currently unclear how its implementation would impact existing stroke networks. Methods Conditional probability models were generated to predict the probability of excellent outcome (modified Rankin Scale 0–1 at 90 days post stroke) for patients with suspected large vessel occlusion (LVO). A baseline stroke network was created for California using existing intravenous thrombolysis (IVT) centers and comprehensive stroke centers (CSCs) capable of IVT and EVT. Optimal transport decisions and catchment areas were generated for the baseline model and three hypothetical scenarios through conversion of IVT centers at various distances from a CSC into centers capable of Telerobotic EVT [i.e. hospitals ≥ 15 and < 50 miles from a CSC were converted (Scenario 1), ≥ 50 and < 100 miles (Scenario 2), and ≥ 100 miles (Scenario 3)]. Catchment areas were calculated for each scenario using varied robotic procedural delay times and success rates. Results In the baseline model, 51 hospitals were designated as CSCs and 142 hospitals were designated IVT centers. Conversion of IVT centers into Telerobotic EVT centers decreased median travel time for LVO patients in all three scenarios. The estimated number of robotically treated LVOs per year in Scenarios 1, 2, and 3 were 2172, 740, and 212, respectively. Scenario 1 (15‐50 miles) was the most sensitive to robotic time delay and success rate, but all three scenarios were more sensitive to decreases in procedural success rate compared to time delay. Conclusions Telerobotic EVT has the potential to improve care for stroke patients outside of major urban centers. Compared to procedural time delays in robotic EVT, a decrease in procedural success rate would not be well tolerated. This modeling analysis can inform system planning for the potential advent of remote EVT care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".