In Search of a Better Outcome: Opting Into the Live Donor Paired Kidney Exchange Program
Bibliographic record
Abstract
Background: Live donor (LD) kidney transplantation is the best option for patients with end-stage kidney disease (ESKD). However, this may not be the best option if a patient’s donor is older and considerably smaller in weight. Patient (A) with a less than ideal donor (Donor A) might enter into a live donor paired exchange (LDPE) program with the hopes of swapping for a better-quality organ. A second patient (B) who is in the LDPE may or may not benefit from this exchange with Donor A. Methods: This medical decision analysis examines the conditions that favor Patient A entering into the LDPE compared to directly accepting a kidney from their intended donor, as well as the circumstances where Patient B also benefits by accepting a lower-quality organ. Results: Under select circumstances, a paired exchange could benefit both Patients A and B. For example, a 30-year-old Patient A with a lower-quality donor might gain 1.20 1.52 1.84 quality adjusted life years (QALYs) by entering into a LDPE for a better-quality kidney, whereas a 60-year-old Patient B might gain 0.93 1.03 1.13 QALYs by accepting Donor A’s kidney rather than waiting longer in the LDPE. The net benefit (or loss) of entering the LDPE differs by recipient age, donor organ quality, likelihood of Patient B being transplanted in LDPE, and likelihood of Patient A finding an ideal donor in the LDPE. Conclusion: This study shows there are ways to increase live donor utilization and effectiveness that require further research and potentially changes to the LDPE process.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".