01-4: DEVELOPMENT OF A DONOR-BASED PREDICTIVE MODEL FOR PANCREAS DISCARD IN DECEASED DONOR TRANSPLANTATION IN THE U.S.
Bibliographic record
Abstract
Introduction: Pancreas discard remains a significant barrier in transplantation, with 25–30% of deceased donor offers discarded in the U.S.1 The European-derived Pre-Procurement Pancreas Suitability Score (P-PASS) has performed variably in external validations.2,3 A robust, donor-based tool is needed to improve utilization and streamline procurement. Methods: We retrospectively analyzed all deceased donor pancreas offers from the U.S. Scientific Registry of Transplant Recipients (2003–2023). The primary outcome was pancreas discard. Candidate predictors included donor demographics, comorbidities, peri-donation variables, and logistical, geographic, and temporal factors. An associative multivariable logistic model identified risk factors using a causal diagram, LASSO selection, and univariable analyses. A parsimonious predictive model for pancreas discard was developed and evaluated for discrimination (area under the curve (AUC)), calibration plots, Brier score, and net reclassification improvement (NRI) versus a modified P-PASS. Internal validation used 1,000 bootstrap resamples. Sensitivity analyses were conducted in sub-cohorts since 2013 and excluding discards unrelated to donor factors. Results: Among 30,757 pancreas offers, 8,048 (26%) were discarded. The predictive model incorporated ten donor factors independently associated with discard: older age, female sex, higher BMI, hypertension, gastrointestinal disease, smoking, donation after cardiac death, stroke as cause of death, elevated terminal creatinine, and abnormal lipase. This model achieved moderate discrimination (AUC 0.699; optimism-corrected 0.698), accurate calibration, a Brier score of 0.090, and a 9.2% NRI over the modified P-PASS. Sensitivity analyses confirmed robust performance: offers since 2013 yielded an AUC of 0.663 (9.7% NRI), and exclusion of non–donor-related discards produced an AUC of 0.732 (11.1% NRI). Conclusions: We developed a novel donor-based predictive model for pancreas discard that demonstrated robust performance in a large North American cohort, improving risk-classification over existing tools. These findings support its potential as a practical decision aid for optimizing donor selection and reducing discard rates, ultimately enhancing outcomes in pancreas 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".