Optimal Transport Scenario With Rotary Air Transport for Access to Endovascular Therapy Considering Patient Outcomes and Cost: A Modeling Study
Bibliographic record
Abstract
Background and Purpose: For an ischemic stroke patient whose onset occurs outside of the catchment area of a hospital that is capable of Endovascular Treatment (EVT) and whose stroke is suspected to be caused by a large vessel occlusion (LVO), a transportation dilemma exists. Bypassing the nearest stroke hospital will delay Alteplase but expedite EVT. Not bypassing allows for confirmation of an LVO diagnosis before transfer to an EVT-enabled facility, but ultimately delays EVT. Air transport can reduce a patient's overall time to treatment however, it is costly. We expanded on an existing model to predict where Drip-and-Ship vs. Mothership provides better outcomes by including rotary air transport, and we also included prediction of where either the transport method was most cost effective. Methods: An existing model predicts the outcome of patients who screen positive for an LVO in the field based on how they were transported, Drip-and-Ship (alteplase-only facility first, then EVT-enabled facility) or Mothership (direct to EVT-enabled facility). In our model, the addition of rotary wing transportation was conditionally applied to inter-facility transfer scenarios where it provided a time advantage. Both patient outcome and transport cost functions were developed for Mothership and Drip-and-Ship strategies including transfers via either ground or air depending on the conditional probabilities. Experiments to model real world scenarios are presented by varying the driving time between the alteplase-only and EVT-enabled facility, time to treatment efficiencies at the alteplase-only facility, and EVT eligibility for LVO patients. Patient outcome and transport costs were evaluated for Mothership and Drip-and-Ship strategies. Results: The results are presented in temporospatial diagrams that are color coded to indicate which strategy optimizes the objectives. In most regions, there was overall agreement between the optimal solution when considering patient outcomes or transport costs. Small regions exist where outcome and cost are divergent; however, the difference between the divergence in Mothership and Drip-and-Ship in these regions is marginal. Conclusions: The optimal transport method can be optimized for both patient outcomes and transport costs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| 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".