Rationing Livers: The Persistence of Geographic Inequity in Organ Allocation
Bibliographic record
Abstract
Whether accurately or not, “rationing ” was frequently employed as a term of opprobrium during both the health reform debates and subsequent explosion in political vituperation. But whatever that word’s lingering stigma, there are unquestionably situations—long preceding the Patient Protection and Affordable Care Act and almost certain to persist whether it is fully implemented or not—in which a fundamental imbalance between need and supply makes rationing of some health services inevitable. In such circumstances, some socially acceptable mechanism must be available to insure equitable and justifiable allocation processes. Yet in the case of liver transplantation, an especially dramatic example of the imbalance between need and supply, neither the public nor the private sector has been able to ensure equitable allocation. Right now, more than 16,000 Americans are awaiting liver transplants, and each year about 10,000 more are determined, as a result of irreversible liver damage, to need one, while for many years the total supply of donated organs has remained relatively steady at about 6,500 [1]. As a nation, we clearly need to do a better job of
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".