Insurance Type and Minority Status Associated with Large Disparities in Prelisting Dialysis among Candidates for Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Disparities in time to placement on the waiting list on the basis of socioeconomic factors decrease access to deceased-donor renal transplantation for some groups of patients with end-stage renal disease. This study was undertaken to determine candidate factors that influence duration of dialysis before placement on the waiting list among candidates for deceased-donor renal transplantation in the United States from January 2001 to December 2004 and the impact of Medicare eligibility rules on access. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Access to the waiting list was measured as the percentage of all wait-listed candidates in the Scientific Registry of Transplant Recipients database who were listed before dialysis and by the duration of dialysis before placement on the waiting list. Multivariate logistic and linear regressions were used to determine variables that were predictive of preemptive listing and the duration of dialysis before listing. RESULTS: The odds for preemptive placement on the waiting list improved during the course of the study period, whereas the median duration of prelisting dialysis did not. The candidate factors that were associated with low rates of preemptive listing and prolonged exposure to prelisting dialysis included Medicare insurance, minority race/ethnicity, and low educational attainment. In patients who were listed after the age of 64 yr, the adverse effect of Medicare insurance on access largely disappeared. CONCLUSIONS: The disparity in dialysis exposure could potentially be diminished by concerted efforts on the part of the nephrology and transplant communities to promote early referral and preemptive placement on the waiting list, by calculating waiting time from the date of initiation of dialysis for patients who are on dialysis at the time of referral, and by relaxing Medicare eligibility requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".