Preemptive Kidney Transplantation
Bibliographic record
Abstract
It remains unclear whether preemptive transplantation is beneficial, and if so, who benefits. A total of 38,836 first, kidney-only transplants between 1995 and 1998 were retrospectively studied. A surprising 39% of preemptive transplants were from cadaver donors, and the proportions of cadaver donor transplants that were preemptive changed little, from 7.3% in 1995 to 7.7% in 1998. Preemptive transplants using cadaver donors were more likely among recipients aged 0 to 17 yr versus 18 to 29 yr (odds ratio [OR], 2.48; 95% confidence interval [CI], 1.94 to 3.17), white versus black (OR, 2.33; 95% CI, 2.03 to 2.68), able to work versus unable to work (OR, 1.42; 95% CI, 1.26 to 1.61), covered by private insurance versus Medicare (OR, 4.77; 95% CI, 4.26 to 5.32), or recipients with a college degree versus no college degree (OR, 1.34; 95% CI, 1.17 to 1.54). Preemptive transplants were less likely for Hispanics versus non-Hispanics (OR, 0.57; 95% CI, 0.50 to 0.67), patients with type 2 versus type 1 diabetes (OR, 0.76; 95% CI, 0.61 to 0.96), and for 2 to 5 HLA mismatches compared with 0 HLA mismatches (OR range, 0.77 to 0.82). In adjusted Cox proportional hazards analysis, the relative risk of graft failure for preemptive transplantation was 0.75 (0.67 to 0.84) among 25,758 cadaver donor transplants and 0.73 (0.64 to 0.83) among 13,078 living donor transplants, compared with patients who received a transplant after already being on dialysis. Preemptive transplantation was associated with a reduced risk of death: 0.84 (0.72 to 0.99) for cadaver donor transplants and 0.69 (0.56 to 0.85) for living donor transplants. Thus, preemptive transplantation, which is associated with improved patient and graft survival, is less common among racial minorities, those who have less education, and those who must rely on Medicare for primary payment. Alterations in the payment system, emphasis on early referral, and changes in cadaver kidney allocation could increase the number of patients who benefit from preemptive 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".