Abstract TMP32: Modeling Optimal Patient Transport In A Stroke Network Capable Of Telerobotic Endovascular Therapy
Bibliographic record
Abstract
Introduction: Endovascular therapy (EVT) is the standard-of-care for large vessel occlusion stroke; yet, a substantial portion of the population lacks timely access to the treatment. Telerobotics has the potential to expand current stroke networks by offering remote EVT to more rural populations. Methods: We geospatially mapped all existing hospitals in California and then assessed hypothetical scenarios in which primary stroke centers between 50 and 100 miles from an existing EVT center offered remote EVT via robotics. Using an existing conditional probability model, we determined the transport scenarios (drip and ship, direct to robotic EVT center, or direct to conventional EVT center) resulting in best patient outcomes in diverse geographies across the state. Timing and the probability of receiving EVT (used as a surrogate for the probability of receiving successful reperfusion) were varied at robotic centers to assess impact on best predicted transport options. Results: 10 hospitals were converted to robotic EVT centers, resulting in enhanced patient access to timely EVT by routing patients in a rural catchment area of 113,063 km 2 directly to robotic EVT centers (figure). Slowing the door to arterial access time at the robotic centers by 30 minutes had a minor impact on optimized service arrangements (reducing the robotic catchment area to 110,555 km 2 ). However, decreasing the probability of receiving EVT at the robotic centers by 0.10 had a substantial impact, reducing the robotic EVT catchment area to 35,061 km 2 and nearly doubling the drip and ship catchment area. Discussion: Telerobotic EVT has the potential to enhance therapy for stroke patients outside of major urban centers. We found that the reduced probability of receiving EVT had a greater impact on the optimized size of robotic EVT center catchment areas compared to longer door to arterial access times. This modeling analysis can inform system planning for the potential advent of remote robotic EVT care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".