The living kidney donor profile index fails to discriminate allograft survival: implications for its use in kidney paired donation programs
Bibliographic record
Abstract
The inclusion of blood group- and human leukocyte antigen-compatible donor and recipient pairs (CPs) in kidney paired donation (KPD) programs is a novel strategy to increase living donor (LD) transplantation. Transplantation from a donor with a better Living Donor Kidney Profile Index (LKDPI) may encourage CP participation in KPD programs. We undertook parallel analyses using data from the Scientific Registry of Transplant Recipients and the Australia and New Zealand Dialysis and Transplant Registry to determine whether the LKDPI discriminates death-censored graft survival (DCGS) between LDs. Discrimination was assessed by the following: (1) the change in the Harrell C statistic with the sequential addition of variables in the LKDPI equation to reference models that included only recipient factors and (2) whether the LKDPI discriminated DCGS among pairs of prognosis-matched LD recipients. The addition of the LKDPI to reference models based on recipient variables increased the C statistic by only 0.02. Among prognosis-matched pairs, the C statistic in Cox models to determine the association of the LKDPI with DCGS was no better than chance alone (0.51 in the Scientific Registry of Transplant Recipient and 0.54 in the Australia and New Zealand Dialysis and Transplant Registry cohorts). We conclude that the LKDPI does not discriminate DCGS and should not be used to promote CP participation in KPD programs.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".