Abstract P529: Optimal Transport Scenario for Access to Endovascular Therapy With Consideration of Patient Outcomes and Cost
Bibliographic record
Abstract
Introduction: For an ischemic stroke patient whose onset geographically occurs outside of the catchment area of an EVT enabled facility and whose stroke is suspected to be caused by an occlusion in a large vessel of the brain, a transportation dilemma exists. Bypassing the nearest stroke hospital will delay tPA but expedite EVT. Not bypassing allows for confirmation of an LVO diagnosis before transfer to a CSC, but ultimately delays EVT. Air transportation can reduce a patient’s overall time to treatment. However, air transportation is costly. Methods: In a previously published model probability functions were developed to predict the outcome of a patient who screened positive for an LVO in the field based on how the patient was transported, Drip and Ship (PSC first, then CSC) or Mothership (direct to CSC). The addition of rotary wing transportation was conditionally applied to inter-facility transfer scenarios where it provided a time advantage. Transportation cost functions were created to include both fixed and variable costs as well as probabilities that model the likelihood of air transport providing a time advantage, air-worthy weather, and air resource availability. Both outcome and cost functions were developed for Mothership scenarios and for Drip and Ship scenarios including transfers via either ground or air depending on the conditional probabilities. Results: The figure shows the results of the model for location scenarios with 60 and 90 minutes between the thrombolysis only center and EVT capable center. Three efficiency scenarios are also shown in the figure: 1) both hospitals are efficient; 2) thrombolysis center is inefficient; and 3) both hospitals are inefficient. Conclusions: In some scenarios, both outcome and cost can be optimized to indicate whether Drip and Ship or Mothership is preferred. However, scenarios exist where outcome and cost are divergent. In divergent scenarios cost can be minimized at the expense of patient outcomes.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".