Health Outcomes for Living Kidney Donors with Isolated Medical Abnormalities: A Systematic Review
Bibliographic record
Abstract
Individuals with isolated medical abnormalities (IMAs) are undergoing living donor nephrectomy more frequently. Knowledge of health risks for these living donors is important for donor selection, informed consent and follow-up. We systematically reviewed studies with > or = 3 living kidney donors with preexisting IMAs, including older age, obesity, hypertension, reduced glomerular filtration rate (GFR), proteinuria, microscopic hematuria and nephrolithiasis. We abstracted data on study and donor characteristics, perioperative outcomes, longer term renal and blood pressure outcomes and mortality and compared them to those of non-IMA donors. We found 22 studies on older donors (n = 987), 10 on obese donors (n = 484), 6 on hypertensive donors (n = 125), 4 on donors with nephrolithiasis (n = 32), 2 on donors with microscopic hematuria and one study each on donors with proteinuria or reduced GFR. Perioperative outcomes for donors with and without IMAs were similar. Few studies reported longer term (> or = 1 year) rates of hypertension, proteinuria or renal function. Studies were frequently retrospective and without a comparison group. Given the variability among studies and their methodological limitations, uncertainties remain regarding long-term medical outcomes for IMA donors. As transplant centers continue to cautiously screen and counsel potential IMA donors, rigorously conducted, longer term prospective cohort studies are needed.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".