Bibliographic record
Abstract
In the past decade, there have been increasing efforts to better define and quantify the short- and long-term risks of living kidney donation. Recent studies have expanded upon the previous literature by focusing on outcomes that are important to potential and previous donors, applying unique databases and/or registries to follow large cohorts of donors for longer periods of time, and comparing outcomes with healthy nondonor controls to estimate attributable risks of donation. Leading outcomes important to living kidney donors include kidney health, surgical risks, and psychosocial effects of donation. Recent data support that living donors may experience a small increased risk of severe CKD and ESKD compared with healthy nondonors. For most donors, the 15-year risk of kidney failure is <1%, but for certain populations, such as young, black men, this risk may be higher. New risk prediction tools that combine the effects of demographic and health factors, and innovations in genetic risk markers are improving kidney risk stratification. Minor perioperative complications occur in 10%-20% of donor nephrectomy cases, but major complications occur in <3%, and the risk of perioperative death is <0.03%. Generally, living kidney donors have similar or improved psychosocial outcomes, such as quality of life, after donation compared with before donation and compared with nondonors. Although the donation process should be financially neutral, living kidney donors may experience out-of-pocket expenses and lost wages that may or may not be completely covered through regional or national reimbursement programs, and may face difficulties arranging subsequent life and health insurance. Living kidney donors should be fully informed of the perioperative and long-term risks before making their decision to donate. Follow-up care allows for preventative care measures to mitigate risk and ongoing surveillance and reporting of donor outcomes to inform prior and future living kidney 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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".