Gout after Living Kidney Donation: Correlations with Demographic Traits and Renal Complications
Bibliographic record
Abstract
BACKGROUND: The demographic and clinical correlates of gout after living kidney donation are not well described. METHODS: Using a unique database that integrates national registry identifiers of U.S. living kidney donors (1987-2007) with billing claims from a private health insurer (2000-2007), we identified post-donation gout based on medical diagnosis codes or pharmacy fills for gout therapies. The frequencies and demographic correlates of gout after donation were estimated by Cox regression with left- and right-censoring. We also compared the rates of renal diagnoses among donors with and without gout, matched in the ratio 1:3 by age, sex, and race. RESULTS: The study sample of 4,650 donors included 13.1% African Americans. By seven years, African Americans were almost twice as likely to develop gout as Caucasian donors (4.4 vs. 2.4%; adjusted hazard ratio, aHR, 1.8; 95% confidence interval (CI) 1.0-3.2). Post-donation gout risk also increased with older age at donation (aHR per year 1.05) and was higher in men (aHR 2.80). Gout rates were similar in donors and age- and sex-matched general non-donors (rate ratio 0.86; 95% CI 0.66-1.13). Compared to matched donors without gout, donors with gout had more frequent renal diagnoses, reaching significance for acute kidney failure (rate ratio 12.5; 95% CI 1.5-107.0), chronic kidney disease (rate ratio 5.0; 95% CI 2.1-11.7), and other disorders of the kidney (rate ratio 2.2; 95% CI 1.2-4.2). CONCLUSION: Donor subgroups at increased risk of gout include African Americans, older donors, and men. Donors with gout have a higher burden of renal complications after demographic adjustment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".