Abstract 85: Drip ‘n Ship vs. Mothership for Endovascular Treatment: Modeling the Best Transportation Options for Optimal Outcomes in California and Alberta
Bibliographic record
Abstract
Background: There is uncertainty about how patients outside of endovascular-capable or Comprehensive Stroke Centers (CSC) access Endovascular treatment (EVT) for acute ischemic stroke. The role of the non-endovascular-capable Primary Stroke Centers (PSC) that can offer thrombolysis with alteplase but not EVT is unclear. A key question is whether average benefit is greater with early thrombolysis at the closest PSC before transportation to the CSC (Drip ‘n Ship), or with PSC by-pass and direct transport to the CSC (Mothership). Ideal transportation options for California, USA and Alberta, Canada were mapped based on the location of their CSCs and PSCs. Methods: For those who received both EVT and alteplase, probability models were developed from the ESCAPE trial’s decay curves for good outcome defined as mRS 0-2 at 90 days. To determine the benefits of alteplase alone, probability models were extracted from the Get-With-The-Guidelines decay curve. The onset to EMS arrival, time on scene, needle-to-door-out time at the PSC, door-to-needle-time (DNT) at the CSC, and door-to-reperfusion time were assumed constant at 30, 25, 20, 30, and 115 minutes, respectively. EMS transportation times were calculated using Google’s Distance Matrix API interfaced with MATLAB’s Mapping Toolbox to create maps demonstrating the transportation scenario resulting in the best outcomes. Six maps were generated for 30, 60, and 90 minute DNT’s at the PSC’s. Results: In the figure, green regions represent a greater probability of good outcome via Mothership, whereas red indicates that Drip ‘n Ship is best. Orange indicates that either option yields a similar outcome (+/- 2.5%). The color tint increases (becomes brighter) as the probability of good outcome decreases. Grey indicates areas with a sparse road network. Conclusions: The role of a PSC in close proximity to a CSC remains significant only when the PSC is able to achieve both a DNT of 30 minutes or less and a needle-to-door out time of 20 minutes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".