Impact of early surgical complications on kidney transplant outcomes
Bibliographic record
Abstract
Abstract Background Kidney transplantation (KT) improves clinical outcomes of patients with end stage renal disease. Little has been reported on the impact of early post-operative surgical complications (SC) on long-term clinical outcomes following KT. We sought to determine the impact of vascular complications, urological complications, surgical site complications, and peri-graft collections within 30 days of transplantation on patient survival, graft function, and hospital readmissions. Methods We conducted a single-centre, observational cohort study examining adult patients (≥ 18 years) who received a kidney transplant from living and deceased donors between January 1st, 2005 and December 31st, 2015 with follow-up until December 31st, 2016 (n = 1,334). Univariable and multivariable analyses were performed with Cox proportional hazards models to analyze the outcomes of SC in the early post-operative period after KT. Results The cumulative probability of SC within 30 days of transplant was 25%, the most common SC being peri-graft collections (66.8%). Multivariable analyses showed significant relationships between Clavien Grade 1 SC and death with graft function (HR 1.78 [95% CI: 1.11, 2.86]), and between Clavien Grades 3 to 4 and hospital readmissions (HR 1.95 [95% CI: 1.37, 2.77]). Conclusions Early SC following KT are common and have a significant influence on long-term patient outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".