Drip ‘n Ship Versus Mothership for Endovascular Treatment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is uncertainty regarding the best way for patients outside of endovascular-capable or Comprehensive Stroke Centers (CSC) to access endovascular treatment for acute ischemic stroke. The role of the nonendovascular-capable Primary Stroke Centers (PSC) that can offer thrombolysis with alteplase but not endovascular treatment 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 bypass and direct transport to the CSC (Mothership). Ideal transportation options were mapped based on the location of their endovascular-capable CSCs and nonendovascular-capable PSCs. METHODS: Probability models for endovascular treatment were developed from the ESCAPE trial's (Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion With Emphasis on Minimizing CT to Recanalization Times) decay curves and for alteplase treatment were extracted from the Get With The Guidelines decay curve. The time on-scene, needle-to-door-out time at the PSC, door-to-needle time at the CSC, and door-to-reperfusion time were assumed constant at 25, 20, 30, and 115 minutes, respectively. Emergency medical services transportation times were calculated using Google's Distance Matrix Application Programming Interface interfaced with MATLAB's Mapping Toolbox to create map visualizations. RESULTS: Maps were generated for multiple onset-to-first medical response times and door-to-needle times at the PSCs of 30, 60, and 90. These figures demonstrate the transportation option that yields the better modeled outcome in specific regions. The probability of good outcome is shown. CONCLUSIONS: Drip 'n Ship demonstrates that a PSC that is in close proximity to a CSC remains significant only when the PSC is able to achieve a door-to-needle time of ≤30 minutes when the CSC is also efficient.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".