A nationwide analysis of re-operation after kidney transplant
Bibliographic record
Abstract
INTRODUCTION: We aimed to report the rate and short-term outcomes of patients undergoing re-operation following kidney transplant in the U.S. METHODS: The Nationwide Inpatient Sample (NIS) database was used to examine the clinical data of patients undergoing kidney transplant and re-operation during same the hospitalization from 2002-2012. Multivariate regression analysis was performed to compare outcomes of patients with and without re-operation. RESULTS: We sampled a total of 35 058 patients who underwent kidney transplant. Of these, 770 (2.2%) had re-operation during the same hospitalization. Re-operation was associated with a significant increase in mortality (30.4% vs. 3%; adjusted odds ratio [AOR] 4.62; p<0.01), mean total hospital charges ($249 425 vs. $145 403; p<0.01), and mean hospitalization length of patients (18 vs. 7 days; p<0.01). The most common day of re-operation was postoperative Day 1. Hemorrhagic complication (64.2%) was the most common reason for re-operation, followed by urinary tract complications (9.9%) and vascular complications (3.6%). Preoperative coagulopathy (AOR 3.35; p<0.01) was the strongest predictor of need for re-operation, hemorrhagic complications (AOR 3.08; p<0.01), and vascular complications (AOR 2.50; p<0.01). Also, hypertension (AOR 1.26; p<0.01) and peripheral vascular disorders (AOR 1.25; p=0.03) had associations with hemorrhagic complications. CONCLUSIONS: Re-operation after kidney transplant most commonly occurs on postoperative Day 1 and occurs in 2.2% of cases. It is associated with significantly increased mortality, hospitalization length, and total hospital charges. Hemorrhage is the most common complication. Preoperative coagulopathy is the strongest factor predicting the need for re-operation, vascular complications, and hemorrhagic 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".