Liver Transplantation: Evolving Patient Selection Criteria
Bibliographic record
Abstract
The widespread recognition of the success of liver transplantation as a treatment for most types of acute and chronic liver failure has led to increased referrals for transplantation in the setting of a relatively fixed supply of cadaver donor organs. These events have led to a marked lengthening of the waiting time for liver transplantation, resulting in increased deaths of those on the waiting list and sicker patients undergoing transplantation. Nearly 5000 liver transplantations were performed in the United States in 2000, while the waiting list grew to over 17,000 patients. The mounting disparity between the number of liver transplant candidates and the limited supply of donor organs has led to reassessment of the selection and listing criteria for liver transplantation, as well as revision of organ allocation and distribution policies for cadaver livers. The development of minimal listing criteria for patients with chronic liver disease based on a specific definition for decompensation of cirrhosis has facilitated the more uniform listing of patients at individual centres across the United States. The United Network for Organ Sharing, under pressure from transplant professionals, patient advocacy groups and the federal government, has continuously revised allocation and distribution policies based on the ethical principles of justice for the individual patient versus optimal utility of the limited organ supply available annually. Beginning in 2002, it is likely that the Model for End-stage Liver Disease (MELD) score will be implemented to determine disease severity and direct donor organs to the sickest patients rather than to those with the longest waiting times.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".