Risk of End-Stage Renal Disease in HIV-Positive Potential Live Kidney Donors
Bibliographic record
Abstract
New federal regulations allow HIV-positive individuals to be live kidney donors; however, potential candidacy for donation is poorly understood given the increased risk of end-stage renal disease (ESRD) associated with HIV infection. To better understand this risk, we compared the incidence of ESRD among 41 968 HIV-positive participants of North America AIDS Cohort Collaboration on Research and Design followed for a median of 5 years with the incidence of ESRD among comparable HIV-negative participants of National Health and Nutrition Examination III followed for a median of 14 years. We used risk associations from multivariable Cox proportional hazards regression to derive cumulative incidence estimates for selected HIV-positive scenarios (no history of diabetes, hypertension, AIDS, or hepatitis C virus coinfection) and compared these estimates with those from similarly selected HIV-negative scenarios. For 40-year-old HIV-positive individuals with health characteristics that were similar to those of age-matched kidney donors, viral load <400 copies/mL, and CD4 + count ≥500 cells/μL, the 9-year cumulative incidence of ESRD was higher than that of their HIV-negative peers, yet still low: 2.5 versus 1.1 per 10 000 among white women, 3.0 versus 1.3 per 10 000 among white men, 13.2 versus 3.6 per 10 000 among black women, and 15.8 versus 4.4 per 10 000 among black men. HIV-positive individuals with no comorbidities and well-controlled disease may be considered low-risk kidney donor candidates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".