Applying Neuroeconomic Principles to Stroke Care: Bridging Evidence to Practice
Bibliographic record
Abstract
Over the past 10 years, stroke care has seen remarkable technological and pharmacological breakthroughs-ranging from advanced thrombectomy devices and streamlined thrombolytic therapies like tenecteplase to artificial intelligence-powered imaging and rehabilitation tools. Yet adoptions remain uneven due to fragmented systems, low adherence, and inconsistent implementation. To overcome these barriers, neuroeconomics-a multifaceted integration of neuroscience, behavioral economics, psychology, and clinical medicine-sharpens decision-making under uncertainty and drives sustained behavior change. Behavioral economics offers a toolkit of low-cost, scalable interventions-nudges, default options, framing effects, commitment devices, incentives, gamification, and social-norm feedback-that can be woven into every phase of stroke management. Embedding preselected treatment orders in electronic health records, default-scheduling follow-up appointments, and delivering tailored digital reminders have all boosted adherence to medications, rehabilitation exercises, and preventive measures. Financial rewards and community-based feedback loops have improved both clinicians' prescribing habits and patient self-management. Early pilot programs demonstrate that even small tweaks in workflow or choice architecture can yield outsized improvements in timely reperfusion, secondary prevention uptake, and long-term outcomes. By embedding evidence-based interventions directly into care decisions, the integration of neuroeconomic principles helps bridge the gap between scientific innovation and its transformative impact on 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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".