Race and renal function early after live kidney donation: an analysis of the United States Organ Procurement and Transplantation Network Database
Bibliographic record
Abstract
Doshi M, Garg AX, Gibney E, Parikh C. Race and renal function early after live kidney donation: an analysis of the United States Organ Procurement and Transplantation Network database. Clin Transplant 2010 DOI: 10.1111/j.1399‐0012.2010.01209.x. © 2010 John Wiley & Sons A/S. Abstract: Among Americans, the risk for kidney disease is higher in individuals of African descent (AA) when compared with Caucasians. We considered whether there are similar racial differences in kidney function soon after donor nephrectomy. Of the 31 928 live kidney donors that donated between the years 2000 and 2005, 16 996 (53%) had post‐donation serum creatinine recorded at a mean follow‐up of 156 d (range 1–1410 d). A total of 14 525 (85%) were Caucasians and 2471 (15%) were AA. When compared with Caucasians, AA donors were more likely to be younger, heavier, and male, had a higher baseline serum creatinine and a shorter duration of follow‐up. After accounting for these differences, the serum creatinine after donation and fractional rise in serum creatinine after donation were similar between the two groups (AA vs. Caucasian donors, 1.3 ± 0.3 vs. 1.2 ± 0.3 mg/dL; 53% vs. 45%) and the post‐donation estimated glomerular filtration rate was also similar (57.2 ± 0.6 vs. 56.0 ± 0.2 mL/min per 1.73 m2). We observed no major clinical difference in glomerular filtration rate and ability to compensate for loss of renal mass soon after live kidney donation between Caucasian and AA donors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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".