Access to Kidney Transplantation among Patients Insured by the United States Department of Veterans Affairs
Bibliographic record
Abstract
Ensuring equal access to kidney transplantation is of paramount importance. Veterans that receive care from the Department of Veteran Affairs (VA) must complete a complex process to be placed on the transplant wait-list, and only four VA hospitals in the United States transplant kidneys. This unique system may cause VA patients to wait longer for kidney transplants than other patients. We compared the time to transplantation among ESRD patients insured by the VA to those insured by private insurance or Medicare/Medicaid. Of 7395 veterans studied, 9.3% received transplants, compared to 35,450 of 144,651 (24.5%) patients with private insurance and 36,150 of 357,345 (10.1%) patients with Medicare/Medicaid insurance (P < 0.0001). We found that both VA-insured and Medicare/Medicaid-insured patients were approximately 35% less likely to receive transplants than patients with private insurance (hazard ratio [HR] 0.65; 95% CI 0.60 to 0.70; P < 0.0001). Most of this difference was explained by the fact that VA patients were less likely to be placed on the wait-list (HR 0.71; 95% CI 0.67 to 0.76), but even listed VA patients received transplants less frequently than those insured privately (HR 0.89; 95% CI 0.82 to 0.96). Interestingly, VA patients with supplemental private insurance had the same likelihood of transplantation as non-VA patients with private insurance. We conclude that VA-insured patients are less likely to receive transplants than privately insured patients, and that further studies are needed to identify the reasons for this disparity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".