Analyzing Association Rules for Graft Failure Following Deceased and Live Donor Kidney Transplantation
Bibliographic record
Abstract
This paper investigates the clinical attributes that contribute to kidney graft failure following live and deceased donor transplantation using an association rule mining approach. The generated rules are used to analyze the distinctive co-occurrence of attributes for those with or without all-cause graft failure. Analysis of a kidney transplantation dataset acquired from the Scientific Registry of Transplant Recipients that included over 95000 deceased and live donor recipients over 5-years was performed. Using an association rule mining approach, we were able to confirm established risk factors for graft loss after live and deceased donor transplantation and identify novel combinations of factors that may have implications for clinical care and risk prediction post kidney transplantation. Using lift as the metric to evaluate association rules, our results indicate that advanced recipient age (i.e. over 60 years), end stage kidney disease due to diabetes, the presence of recipient peripheral vascular disease and recipient coronary artery disease have a high likelihood of graft failure within 5 years after transplantation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".