Race, Relationship and Renal Diagnoses After Living Kidney Donation
Bibliographic record
Abstract
BACKGROUND: In response to recent studies, a better understanding of the risks of renal complications among African American and biologically related living kidney donors is needed. METHODS: We examined a database linking U.S. registry identifiers for living kidney donors (1987-2007) to billing claims from a private health insurer (2000-2007 claims) to identify renal condition diagnoses categorized by International Classification of Diseases 9th Revision coding. Cox regression with left and right censoring was used to estimate cumulative incidence of diagnoses after donation and associations (adjusted hazards ratios, aHR) with donor traits. RESULTS: Among 4650 living donors, 13.1% were African American and 76.3% were white; 76.1% were first-degree relatives of their recipient. By 7 years post-donation, after adjustment for age and sex, greater proportions of African American compared with white donors had renal condition diagnoses: chronic kidney disease (12.6% vs 5.6%; aHR, 2.32; 95% confidence interval [95% CI], 1.48-3.62), proteinuria (5.7% vs 2.6%; aHR, 2.27; 95% CI, 1.32-3.89), nephrotic syndrome (1.3% vs 0.1%; aHR, 15.7; 95% CI, 2.97-83.0), and any renal condition (14.9% vs 9.0%; aHR, 1.72; 95% CI, 1.23-2.41). Although first-degree biological relationship to the recipient was not associated with renal risk, associations of African American race persisted for these conditions and included unspecified renal failure and reported disorders of kidney dysfunction after adjustment for biological donor-recipient relationship. CONCLUSIONS: African Americans more commonly develop renal conditions after living kidney donation, independent of donor-recipient relationship. Continued research is needed to improve risk stratification for renal outcomes among African American living donors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".