The differential impact of size mismatch in live versus deceased donor kidney transplant
Bibliographic record
Abstract
BACKGROUND: The impact of weight mismatch between donors and recipients (D-R) undergoing living-donor kidney transplant (LDKT) versus weight-matched deceased donor kidney transplant (DDKT) is not established. AIM: To determine whether absolute weight mismatch between D-R affects graft survival following LDKT and how this relates to graft outcomes with DDKT when D-R are weight matched. MATERIALS & METHODS: We used multivariable Cox proportional hazards models and the Scientific Registry of Transplant Recipients to determine the association of weight-mismatched D-R (>50 kg, 30-50 kg or 10-30 kg ((D < R); (D > R) and <10 kg (D = R)) with death-censored graft failure in US LDKT recipients from 2006 to 2017. We also explored outcomes relative to weight-matched DDKT and finally, the impact of combined D-R weight-sex mismatch. RESULTS: In LDKT, the risk of graft loss was highest in the setting of D < R (HR 1.28, 95% CI 1.05-1.56 for >50 kg difference relative to D = R); however, this was still lower risk than weight-matched DDKT. D-R sex and combined weight-sex mismatch were only important for male recipients (HR 1.47, 95% CI 1.27-1.71 for a male recipient >30 kg larger than their female donor, relative to weight-matched male donor-male recipient). This remained superior to weight-sex-matched DDKT however. CONCLUSION: D-R weight-sex mismatch is important in LDKT; however, graft survival remains superior to proceeding with matched DDKT. Optimizing D-R matching in LDKT could be facilitated through a national kidney-paired donation registry. LDKT weight-sex mismatch should not be deferred in favor of DDKT.
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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 | 0.001 |
| 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".