Modeling Stroke Patient Transport for All Patients With Suspected Large-Vessel Occlusion
Bibliographic record
Abstract
Importance: Ischemic stroke with large-vessel occlusion can be treated with alteplase and/or endovascular therapy; however, the administration of each treatment is time sensitive. Objective: To identify the optimal triage and transport strategy: direct to the endovascular center (mothership) or immediate alteplase treatment followed by transfer to the endovascular center (drip and ship), for all patients with suspected large-vessel occlusion stroke. Design Setting, and Participants: This was a theoretical, conditional probability modeling study. Existing data from clinical trials of stroke treatment were used for model generation. The study was conducted from February 1, 2017, to March 1, 2018. Main Outcomes and Measures: The time-dependent efficacy of alteplase and endovascular therapy and the accuracy of large-vessel occlusion screening tools were modeled to estimate the probability of positive outcome (modified Rankin Scale score, 0-1 at 90 days) for both the drip-and-ship and mothership transport strategies. Based from onset to treatment, the strategy that estimates the greatest probability of excellent outcome is determined in several different scenarios. Results: The patient's travel time from both thrombolysis and endovascular therapy centers, speed of treatment, and positive predictive value of the screening tool affect whether the drip-and-ship or mothership strategy estimates best outcomes. With optimal treatment times (door-to-needle time: 30 minutes; door-in-door-out time: 50 minutes; door-to-groin-puncture time: 60 minutes [mothership], 30 minutes [drip and ship]), both options estimate similar outcomes when the centers are 60 minutes or less apart. However, with increasing travel time between the 2 centers (90 or 120 minutes), drip and ship is favored if the patient would have to travel past the thrombolysis center to reach the endovascular therapy center or if the patient would arrive outside the alteplase treatment time window in the mothership scenario. Holding other variables constant, if treatment times are slow at the thrombolysis center (door-to-needle time: 60 minutes; door-in-door-out time: 120 minutes), the area where mothership estimates the best outcomes expands, especially when the 2 centers are close together (60 minutes apart or less). The area where mothership estimates the best outcome also expands as the positive predictive value of the screening tool increases. Conclusions and Relevance: This study suggests that decision making for prehospital transport can be modeled using existing clinical trial data and that these models can be dynamically adapted to changing realities. Based on current median treatment times to realize the full benefit of endovascular therapy on a population level, the study findings suggest that delivery of the treatment should be regionally centralized. The study modeling suggests that transport decision making is context specific and the radius of superiority of the transport strategy changes based on treatment times at both centers, transport times, and the triaging tool used.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".