The Change in Living Kidney Donation in Women and Men in the United States (2005–2015): A Population-Based Analysis
Bibliographic record
Abstract
The factors underlying the decline in living kidney donation in the United States since 2005 must be understood to inform strategies to ensure access to this option for future patients. Population-based estimates provide a better assessment of donation activity than do trends in the number of living donor transplants. Using data from the Scientific Registry of Transplant Recipients and the United States Census, we determined longitudinal changes in living kidney donation between 2005 and 2015, focusing on the effect of sex and income. We used multilevel Poisson models to adjust for differences in age, race, the incidence of ESRD, and geographic factors (including population density, urbanization, and daily commuting). During the study period, the unadjusted rate of donation was 30.1 and 19.3 per million population in women and men, respectively, and the adjusted incidence of donation was 44% higher in women (incidence rate ratio [IRR], 1.44; 95% confidence interval [95% CI], 1.39 to 1.49). The incidence of donation was stable in women (IRR, 0.95; 95% CI, 0.84 to 1.07) but declined in men (IRR, 0.75; 95% CI, 0.68 to 0.83). Income was associated with longitudinal changes in donation in both sexes, yet donation was stable in the highest two population income quartiles in women but only in the highest income quartile in men. In both sexes, living related donations declined, irrespective of income. In conclusion, living donation declined in men but remained stable in women between 2005 and 2015, and income appeared to have a greater effect on living donation in men.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".