Applying principles from the game theory to acute stroke care: Learning from the prisoner’s dilemma, stag-hunt, and other strategies
Bibliographic record
Abstract
BACKGROUND: Acute stroke care represents a challenge for decision makers. Decisions based on erroneous assessments may generate false expectations of patients and their family members, and potentially inappropriate medical advice. Game theory is the analysis of interactions between individuals to study how conflict and cooperation affect our decisions. AIMS: We reviewed principles of game theory that could be applied to medical decisions under uncertainty. SUMMARY: Medical decisions in acute stroke care are usually made under constrains: short period of time, with imperfect clinical information, limit understanding about patients and families' values and beliefs. Game theory brings some strategies to help us manage complex medical situations under uncertainty. For example, it offers a different perspective by encouraging the consideration of different alternatives through the understanding of patients' preferences and the careful evaluation of cognitive distortions when applying 'real-world' data. The stag-hunt game teaches us the importance of trust to strength cooperation for a successful patient-physician interaction that is beyond a good or poor clinical outcome. CONCLUSIONS: The application of game theory to stroke care may improve our understanding of complex medical situations and help clinicians make practical decisions under uncertainty.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".