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Record W3137606531 · doi:10.1161/str.52.suppl_1.p529

Abstract P529: Optimal Transport Scenario for Access to Endovascular Therapy With Consideration of Patient Outcomes and Cost

2021· article· en· W3137606531 on OpenAlexaff
Ashlee Wheaton, Peter T. Vanberkel, David Volders, Patrick T. Fok, Jessalyn K. Holodinsky, Noreen Kamal

Bibliographic record

VenueStroke · 2021
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsSunnybrook HospitalSunnybrook Health Science CentreNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicineThrombolysisFixed wingOutcome (game theory)EngineeringCardiologyAerospace engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.022
GPT teacher head0.264
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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