Modeling the Optimal Transportation for Acute Stroke Treatment
Bibliographic record
Abstract
Background and Purpose— Health systems are faced with the challenge of ensuring fast access to appropriate therapy for patients with acute stroke. The paradigms primarily discussed are mothership and drip and ship. Less attention has been focused on the drip-and-drive (DD) paradigm. Our aim was to analyze whether and under what conditions DD would predict the greatest probability of good outcome for patients with suspected ischemic stroke in Northwestern Germany. Methods— Conditional probability models based on the decay curves for endovascular therapy and intravenous thrombolysis were created to determine the best transport paradigm, and results were displayed using map visualizations. Our study area consisted of the federal states of Lower Saxony, Hamburg, and Schleswig-Holstein in Northwestern Germany covering an area of 64 065 km 2 with a population of 12 703 561 in 2017 (198 persons per km 2 ). In several scenarios, the catchment area, that is, the region that would result in the greatest probability of good outcomes, was calculated for each of the mothership, drip-and-ship, and the DD paradigms. Several different treatment time parameters were varied including onset-to-first-medical-response time, ambulance-on-scene time, door-to-needle time at primary stroke center, needle-to-door time, door-to-needle time at comprehensive stroke center, door-to-groin-puncture time, needle-to-interventionalist-leave time, and interventionalist-arrival-to-groin-puncture time. Results— The mothership paradigm had the largest catchment area; however, the DD catchment area was larger than the drip-and-ship catchment area so long as the needle-to-interventionalist-leave time and the interventionalist-arrival-to-groin-puncture time remain <40 minutes each. A slowed workflow in the DD paradigm resulted in a decrease of the DD catchment area to 1221 km 2 (2%). Conclusions— Our study suggests the largest catchment area for the mothership paradigm and a larger catchment area of DD paradigm compared with the drip-and-ship paradigm in Northwestern Germany in most scenarios. The existence of different paradigms allows the spread of capacities, shares the cost and hospital income, and gives primary stroke centers the possibility to provide endovascular therapy services 24/7.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".