The Long-Term Quality of Life of Living Kidney Donors: A Multicenter Cohort Study
Bibliographic record
Abstract
Previous studies that described the long-term quality of life of living kidney donors were conducted in single centers, and lacked data on a healthy nondonor comparison group. We conducted a retrospective cohort study to compare the quality of life of 203 kidney donors with 104 healthy nondonor controls using validated scales (including the SF36, 15D and feeling thermometer) and author-developed questions. Participants were recruited from nine transplant centers in Canada, Scotland and Australia. Outcomes were assessed a median of 5.5 years after the time of transplantation (lower and upper quartiles of 3.8 and 8.4 years, respectively). 15D scores (scale of 0 to 1) were high and similar between donors and nondonors (mean 0.93 (standard deviation (SD) 0.09) and 0.94 (SD 0.06), p = 0.55), and were not different when results were adjusted for several prognostic characteristics (p = 0.55). On other scales and author-developed questions, groups performed similarly. Donors to recipients who had an adverse outcome (death, graft failure) had similar quality of life scores as those donors where the recipient did well. Our findings are reassuring for the practice of living transplantation. Those who donate a kidney in centers that use routine pretransplant donor evaluation have good long-term quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".