Early Clinical and Economic Outcomes of Patients Undergoing Living Donor Nephrectomy in the United States
Bibliographic record
Abstract
BACKGROUND: Efforts to maximize kidney transplantation are tempered by concern for the live donor's safety. Case series and center surveys exist, but national aggregate data are lacking. We sought to determine predictors of early clinical and economic outcomes following living donor nephrectomy. DESIGN: A retrospective cross-sectional analysis using 1999-2005 discharge data from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample was performed. Cases were identified by International Classification of Diseases, Ninth Revision (ICD-9), codes. Clinical and economic outcomes were analyzed with regard to patient and provider characteristics using bivariate and multivariate regression analyses. SETTING: Healthcare Cost and Utilization Project Nationwide Inpatient Sample. PATIENTS: Patients undergoing living donor nephrectomy, identified by the ICD-9 codes. INTERVENTIONS: Clinical and economic outcomes were analyzed with regard to patient and provider characteristics using bivariate and multivariate regression analyses. MAIN OUTCOME MEASURES: In-hospital complications, mortality, mean length of stay (LOS), and mean total hospital costs. RESULTS: A total of 6320 cases were identified with 0% mortality and a complication rate of 18.4%. The mean (SD) LOS was 3.3 (0.3) days, and the mean inpatient cost was $10 708 ($505). Independent predictors of donor complications included older age (odds ratio [OR], 1.01), male sex (OR, 1.19), Charlson Comorbidity Index of at least 1 (OR, 1.49), obesity (OR, 1.76), medium-size hospitals (OR, 1.88), and low-volume hospitals (OR, 1.37). Predictors of longer LOS included older age, female sex, Charlson score of at least 1, lower household income, low-volume and urban hospitals, and low-volume surgeons. CONCLUSIONS: Kidney donation is associated with a low mortality rate but an 18% complication rate. Donation by those with advanced age or obesity is associated with higher risks. Informed consent should include discussion of these risks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".