Predictive Ability of Pretransplant Comorbidities to Predict Long-Term Graft Loss and Death
Bibliographic record
Abstract
Whether to include additional comorbidities beyond diabetes in future kidney allocation schemes is controversial. We investigated the predictive ability of multiple pretransplant comorbidities for graft and patient survival. We included first-kidney transplant deceased donor recipients if Medicare was the primary payer for at least one year pretransplant. We extracted pretransplant comorbidities from Medicare claims with the Clinical Classifications Software (CCS), Charlson and Elixhauser comorbidities and used Cox regressions for graft loss, death with function (DWF) and death. Four models were compared: (1) Organ Procurement Transplant Network (OPTN) recipient and donor factors, (2) OPTN + CCS, (3) OPTN + Charlson and (4) OPTN + Elixhauser. Patients were censored at 9 years or loss to follow-up. Predictive performance was evaluated with the c-statistic. We examined 25 270 transplants between 1995 and 2002. For graft loss, the predictive value of all models was statistically and practically similar (Model 1: 0.61 [0.60 0.62], Model 2: 0.63 [0.62 0.64], Models 3 and 4: 0.62 [0.61 0.63]). For DWF and death, performance improved to 0.70 and was slightly better with the CCS. Pretransplant comorbidities derived from administrative claims did not identify factors not collected on OPTN that had a significant impact on graft outcome predictions. This has important implications for the revisions to the kidney allocation scheme.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".