Donor Tissue Characteristics Influence Cadaver Kidney Transplant Function and Graft Survival but Not Rejection
Bibliographic record
Abstract
Acute injury and age are characteristics of transplanted tissue that influence many aspects of the course of a renal allograft. The influence of donor tissue characteristics on outcomes can be analyzed by studying pairing, the extent to which two kidneys retrieved from the same cadaver donor manifest similar outcomes. Pairing studies help to define the relative role of donor-related factors (among pairs) versus non-donor factors (within pairs). This study analyzed graft survival for 220 pairs of cadaveric kidneys for the similarity of parameters reflecting function and rejection. It also examined whether the performance of one kidney was predicted by the course of its "mate," the other kidney from that donor. Parameters reflecting function showed sustained pairing posttransplantation, as did graft survival. In contrast, measures of rejection strongly affected survival but showed no pairing. Surprisingly, the survival of a kidney was predicted by the early performance of its mate, an observation we term the "mate effect." Six-month graft survival and renal function were reduced in grafts for which the mate kidney displayed any criteria for functional impairment (dialysis dependency, low urine output [</=1 L] in the first 24 h posttransplant or day-7 serum creatinine >/= 400 micro mol/L), even for kidneys which themselves lacked those criteria. Rejection measures did not demonstrate the mate effect. In conclusion, kidney transplant function is strongly linked to donor-related factors (age, brain death). In contrast, rejection affects survival and function, but it is not primarily determined by the characteristics of the donor tissue. Graft survival reflects both of these influences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".