The System of Health Insurance for Living Donors Is a Disincentive for Live Donation
Bibliographic record
Abstract
The health insurance system for living donors is derived from insurance policies designed to cover accidental death or dismemberment. The system covers only the direct consequences of organ removal, and recoups the costs of related medical services from the transplant recipient's health insurance provider. The system forces transplant programs to differentiate between health services that are, or are not directly attributable to donation and may compromise the pretransplant evaluation, postoperative care and long-term care of living donors. The system is particularly problematic in the United States, where a significant proportion of donors do not have medical insurance. The requirement to assign donor costs to a particular recipient is poorly suited to facilitate advances in living donation such as the use of nondirected donors and living-donor paired exchange programs. We argue that given the current understanding regarding the long-term risks of living donation, the provision of basic medical insurance is a necessity for living donation and that the system of attributing donor costs to the recipient's insurance is inefficient, has the potential to undermine the care of living donors and is a disincentive to the expansion of living donation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".