Developing Statistical Models to Assess Transplant Outcomes Using National Registries
Bibliographic record
Abstract
Created by the US National Organ Transplant Act in 1984, the Scientific Registry of Transplant Recipients (SRTR) is obligated to publicly report data on transplant program and organ procurement organization performance in the United States. These reports include risk-adjusted assessments of graft and patient survival, and programs performing worse or better than expected are identified. The SRTR currently maintains 43 risk adjustment models for assessing posttransplant patient and graft survival and, in collaboration with the SRTR Technical Advisory Committee, has developed and implemented a new systematic process for model evaluation and revision. Patient cohorts for the risk adjustment models are identified, and single-organ and multiorgan transplants are defined, then each risk adjustment model is developed following a prespecified set of steps. Model performance is assessed, the model is refit to a more recent cohort before each evaluation cycle, and then it is applied to the evaluation cohort. The field of solid organ transplantation is unique in the breadth of the standardized data that are collected. These data allow for quality assessment across all transplant providers in the United States. A standardized process of risk model development using data from national registries may enhance the field.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".