Kidney paired donation: principles, protocols and programs
Bibliographic record
Abstract
Due to the ongoing shortage of deceased-donor organs, novel strategies to augment kidney transplantation rates through expanded living donation strategies have become essential. These include desensitization in antibody-incompatible transplants and kidney paired donation (KPD) programs. KPD enables kidney transplant candidates with willing but incompatible living donors to join a registry of other incompatible pairs in order to find potentially compatible transplant solutions. Given the significant immunologic barriers with fewer donor options, single-center or small KPD programs may be less successful in transplanting the more sensitized patients; the optimal solution for the difficult-to-match patient is access to more potential donors and large multicenter or national registries are essential. Multicenter KPD programs have become common in the last decade, and now represent one of the most promising opportunities to improve transplant rates. To maximize donor-recipient matching, and minimize immunologic risk, these multicenter KPD programs use sophisticated algorithms to identify optimal match potential, with simultaneous two-, three- or more complex multiway exchanges. The article focuses on the recent progresses in KPD and it also reviews some of the differences and commonalities across four different national KPD programs.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".