How does the 6-month abstinence period fare for patients seeking Liver transplantation? Lessons Learned from Game Theoretic Analysis
Bibliographic record
Abstract
Abstract Background Patients with alcoholic liver disease are often required to demonstrate a period of abstinence before being eligible for liver transplant. This is known as the 6-month rule. Early liver transplant has shown comparable outcomes in carefully selected patients, yet the 6-month rule is still used in transplant centers worldwide. We applied game theory to evaluate whether the 6-month rule is effective in distinguishing a relapsing patient from a non-relapsing patient during decisions for liver transplant for alcoholic liver diseases. Methods We used game theory to model the interactions between alcoholic liver disease patients and transplant physicians. We assumed that patients are either curable or refractory, but the physician does not know which. Patients can either abstain for 6 months or not, thereby signaling their type to the physician. We solved this model for the equilibria under different payoff assumptions. Results The equilibria for the models for both patient types resulted in the same equilibria, indicating that the 6-month rule is ineffective in separating the two types of patients. This finding held true for all probabilities of patient relapse and all payoff structures except the unlikely scenario where the cost of abstinence outweighs the benefits of transplant. Limitations Our model is based on assumptions, though these assumptions reflect real world preferences and scenarios. We also elected not to analyze other critical factors in the decision-making process for liver transplantation such as the patient’s clinical profile or the physician’s pre-existing biases. Conclusions Our game theoretic framework offers a mathematical explanation on why the 6-month abstinence criterion, although seemingly intuitive, is not an effective strategy for identifying LT candidates who would develop an alcohol relapse.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".