Abstract TMP49: Social Network Simulation Identifies Persistent Racial Disparities of Delay to Hospital in Acute Ischemic Stroke Patients
Bibliographic record
Abstract
Introduction: Delayed arrival to the hospital remains the major reason for non-use of stroke therapies. Minority patients have longer delays that have not been adequately understood nor acted upon. Social context plays a key role, because most strokes occur in front of witnesses who influence decision-making. We created a social network simulation to understand the interpersonal factors that influence decision-making, particularly in minority patients. Methods: We developed an agent-based computer simulation of individual traits and social contextual factors that contribute to decision-making. The inputs into this parsimonious model were based on the largest empirical studies (e.g., Get With The Guidelines) and included: stroke severity, use of Emergency Medical Services (EMS), race, and social network size. The model outputs were the number of stroke patients who arrived at the hospital early (≤3 hours) and late (>3 hours). For each run of the model, 1,000 stroke patients decided to go to the hospital early or late based on individual and social contextual factors. Two sample t-tests were used to compare means between white and non-white patients. Results: In 1.03 million simulations, the model reproduced observed population trends of delay. The overall mean percent of early arrivers was 25.2% (SD 0.02), which matched national metrics showing good calibration of the model. Race predicted delay and modified the relationship of the main effects. 17.9% of non-white patients arrived early compared to 28.3% white patients (p<0.0001) (Fig 1-A). 17.6% of non-white patients with moderate strokes arrived early compared to 39.8% of white patients (p<0.0001) (Fig 1-B). 18.8% of non-white patients with a social network size of 10 arrived early compared to 31.5% of white patients (p<0.0001) (Fig 1-C). Conclusion: A social network simulation reproduced persistent racial disparities of delayed arrival allowing for novel interventions to be tested on this platform.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".