Perioperative Complications After Living Kidney Donation: A National Study
Bibliographic record
Abstract
We integrated the US transplant registry with administrative records from an academic hospital consortium (97 centers, 2008-2012) to identify predonation comorbidity and perioperative complications captured in diagnostic, procedure, and registry sources. Correlates (adjusted odds ratio, aOR) of perioperative complications were examined with multivariate logistic regression. Among 14 964 living kidney donors, 11.6% were African American. Nephrectomies were predominantly laparoscopic (93.8%); 2.4% were robotic and 3.7% were planned open procedures. Overall, 16.8% of donors experienced a perioperative complication, most commonly gastrointestinal (4.4%), bleeding (3.0%), respiratory (2.5%), surgical/anesthesia-related injuries (2.4%), and "other" complications (6.6%). Major Clavien Classification of Surgical Complications grade IV or higher affected 2.5% of donors. After adjustment for demographic, clinical (including comorbidities), procedure, and center factors, African Americans had increased risk of any complication (aOR 1.26, p = 0.001) and of Clavien grade II or higher (aOR 1.39, p = 0.0002), grade III or higher (aOR 1.56, p < 0.0001), and grade IV or higher (aOR 1.56, p = 0.004) events. Other significant correlates of Clavien grade IV or higher events included obesity (aOR 1.55, p = 0.0005), predonation hematologic (aOR 2.78, p = 0.0002) and psychiatric (aOR 1.45, p = 0.04) conditions, and robotic nephrectomy (aOR 2.07, p = 0.002), while annual center volume >50 (aOR 0.55, p < 0.0001) was associated with lower risk. Complications after live donor nephrectomy vary with baseline demographic, clinical, procedure, and center factors, but the most serious complications are infrequent. Future work should examine underlying mechanisms and approaches to minimizing the risk of perioperative complications in all 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".