Perioperative Donor Nephrectomy Risks in Living Kidney Donors with Obesity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Obesity is a global public health concern. Given the shortage of living kidney donations, transplant centers have been more willing to accept obese donor candidates in recent years. To better counsel obese donor candidates about donor nephrectomy risks, we aimed to summarize evidence for the perioperative risks for obese living kidney donors compared to non-obese living kidney donors across studies. Methods: A systematic search of standard databases was conducted to identify studies comparing obese and non-obese living kidney donors. Outcome data were extracted and synthesized. Obesity was defined as BMI ≥ 30. The risk of bias was assessed using Cochrane's risk of bias in non-randomized studies - of interventions (ROBINS-I) tool. Results: Fifteen cohort studies were included in the final review. Obese patients have significantly longer operative times compared to non-obese patients, with a standardized mean difference of 0.93 (95% CI: 0.21 to 1.65, P=0.01), favoring non-obese donors (Figure 1). Additionally, Obese donors have significantly higher odds of surgical complications compared to non-obese patients, with an odds ratio of 1.22 (95% CI: 1.00 to 1.48, P=0.05), favoring non-obese patients (Figure 2). Conclusion: Living kidney donors with obesity have increased risks of longer operating time and perioperative complications. These findings highlight the need for interventions to minimize perioperative risk for obese donors and tailored follow-up care to ensure best outcomes for this group of donors, who provide a vital source for living kidney donation. Funding: NIDDK SupportOperative timeSurgical complications
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".