Complications in the 90-day postoperative period following kidney transplant and the relationship of the Charlson Comorbidity Index
Bibliographic record
Abstract
INTRODUCTION: Renal transplant experiences widespread success, but little is published regarding the postoperative complications. The Charlson Comorbidity Index (CCI) is a system of mortality risk assessment. Our purpose is to assess the 90-day postoperative complications after renal transplantation. The secondary objective is to clarify whether CCI predicts complications. We hypothesized increased CCI corresponds to worse complication on the Clavien scale. METHODS: This is a retrospective analysis of renal recipients at our institution (2011-2013) who were ≥18 years old and received complete follow up. CCI, age, gender, body mass index (BMI), and graft type were extracted from the electronic medical records. Complications were scored using the Clavien scale. Descriptive statistics and logistic regression were used to analyze 198 patients. RESULTS: The mean age was 53 (standard deviation [SD] 14), mean BMI 27.4 (SD 14), median CCI 1. Grade 2 or higher (significant) complications occurred in 60% of patients and Grade 3b or higher (severe) in 15% of patients in the 90-day postoperative period. Sixty-eight different complications were identified, the most common being blood transfusion (19%). Logistic regression suggests a predictive value of CCI (odds ratio [OR] 1.70; 95% confidence interval [CI] 1.3-2.3) for severe complications, with diabetes mellitus and peripheral vascular disease conferring increased risk. CONCLUSIONS: Renal transplant carries significant risk. This data can be used to improve patient counselling on the likely postoperative course. Study limitations include the retrospective design, predisposing to potential bias in data capture.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".