Donor-Recipient Matching for Kidney Transplantation Using Uncertainty Estimation in Generalized Propensity Score
Bibliographic record
Abstract
Kidney donor-recipient matching is a complex process influenced by various clinical and demographic factors, requiring advanced techniques for effective allocation. This study explores the use of Generalized Propensity Score (GPS) modeling with uncertainty estimation and Sufficient Dimensionality Reduction (SDR) methods to enhance matching strategies. We evaluated several dimensionality reduction techniques using Root Mean Squared Error (RMSE) and Pearson Correlation (PC). The regression method (MLP) showed the best performance with the lowest RMSE (1163) and highest PC (0.23) with single dimension. For uncertainty estimation, Quantile Regression outperformed Ensemble and Bayesian MC Dropout methods, with a positive difference of 2695 in mean squared errors between uncertain and certain subsets, indicating its reliability in uncertainty assessment. Our findings establish the benefits of SDR methods for preserving causal relationships and demonstrate that uncertainty-based subset analysis can enhance counterfactual analysis through advanced techniques like propensity score matching and adversarial learning.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".