Access to Kidney Transplantation among the Elderly in the United States
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Few elderly ESRD patients are ever wait-listed for deceased-donor transplantation (DDTX), and waiting list outcomes may not reflect access to transplantation in this group. Our objective was to determine longitudinal changes in access to transplantation among all elderly patients with ESRD, not just those wait-listed for DDTX. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using data from the US Renal Data System, we determined changes in the adjusted likelihood of transplantation from any donor source as an indicator of access to transplantation among all incident ESRD patients aged 60 to 75 years between 1995 and 2006. RESULTS: Access to transplantation doubled between 1995 and 2006 despite an apparent decrease in the likelihood of DDTX after wait-listing. A threefold increase in the likelihood of living-donor transplantation, including a 1.5-fold increase in living-donor transplantation after wait-listing, was a key factor that led to increased access to transplantation. When a lead-time bias related to the increased practice of placing patients on the waiting list before dialysis initiation in more recent years was accounted for, there was no decrease in the likelihood of DDTX after wait-listing. The likelihood of receiving a DDTX after placement on the waiting list was maintained by a threefold increase in expanded-criteria-donor transplantation and a 26% reduction in the risk for death on the waiting list. CONCLUSIONS: Although transplantation remains infrequent, elderly patients were twice as likely to undergo transplantation in 2006 versus 1995. Elderly patients with ESRD should not be dissuaded from pursuing 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".