Prediction of kidney graft failure using clinical scoring tools
Bibliographic record
Abstract
BACKGROUND: Donor organ quality is a key determinant of graft function, and considerable efforts have been made to identify donor and transplant factors predicting inferior outcomes. This has resulted in the development of various scoring tools to aid in allocation of kidneys. METHODS: The performance of four donor quality scoring systems in predicting delayed graft function, and death-censored graft failure was examined in a single-center cohort of 730 consecutive deceased donor kidneys transplanted between 1990 and 2004. The predictive accuracy of the variables was analyzed with receiver operating characteristic curves and graft survival distribution. RESULTS: The three outcome tools, that is, deceased donor score (DDS; Am J Transplant, 3, 2003, 715), donor risk score (DRS; Am J Transplant, 5, 2005, 757) and kidney donor risk index (KDRI; Transplantation, 88, 2009, 231) provided a significant and equivalent prediction of graft failure by using variables available at time of transplantation (p < 0.01). The risk of delayed graft function was predicted by the (DGF) nomogram (J Am Soc Nephrol, 14, 2003, 2967; Am J Transplant 10, 2010, 2279) with a high degree of discrimination (concordance index of 0.69, p < 0.01). CONCLUSIONS: Our findings validate four pre-operative clinical scoring tools to predict early and late graft outcome in an independent, single-center set of kidney transplants.
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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) | 0.001 | 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".