Lifetime risks of kidney donation: a medical decision analysis
Bibliographic record
Abstract
OBJECTIVE: This study estimated the potential loss of life and the lifetime cumulative risk of end-stage renal disease (ESRD) from live kidney donation. DESIGN: Markov medical decision analysis. SETTING: USA. PARTICIPANTS: 40-year-old live kidney donors of both sexes and black/white race. INTERVENTION: Live donor nephrectomy. MAIN OUTCOME AND MEASURES: Potential remaining life years lost, quality-adjusted life years (QALYs) lost and added lifetime cumulative risk of ESRD from donation. RESULTS: Overall 0.532-0.884 remaining life years were lost from donating a kidney. This was equivalent to 1.20%-2.34% of remaining life years (or 0.76%-1.51% remaining QALYs). The risk was higher in male and black individuals. The study showed that 1%-5% of average-age current live kidney donors might develop ESRD as a result of nephrectomy. The added risk of ESRD resulted in a loss of only 0.126-0.344 remaining life years. Most of the loss of life was predicted to be associated with chronic kidney disease (CKD) not ESRD. Most events occurred 25 or more years after donation. Reducing the increased risk of death associated with CKD had a modest overall effect on the per cent loss of remaining life years (0.72%-1.9%) and QALYs (0.58%-1.33%). Smoking and obesity reduced life expectancy and increased overall lifetime risks of ESRD in non-donors. However the percentage loss of remaining life years from donation was not very different in those with or without these risk factors. CONCLUSION: Live kidney donation may reduce life expectancy by 0.5-1 year in most donors. The development of ESRD in donors may not be the only measure of risk as most of the predicted loss of life predates ESRD. The study identifies the potential importance of following donors and treating risk factors aggressively to prevent ESRD and to improve donor survival.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".