Risk Factors of Surgical Site Infections after Simultaneous Kidney Pancreas (SKP) Transplantation
Bibliographic record
Abstract
Kidney and pancreas transplantation is a preferred treatment modality to ameliorate the renal failure and other comorbidities associated with type I diabetes. Postoperative surgical site infections (SSIs) and urinary tract infections have been noted to be the most common infections seen after SKP transplantation. This study assessed the incidence of SSIs and risk factors for these SSIs in SKP transplant recipients within the first three months after transplantation. This retrospective, single-center, cohort study was conducted at the Toronto General Hospital of the University Health Network, Toronto, Canada from January 2000 to December 2015. SSIs were classified according to the Centers for Disease Control classification as superficial, deep and organ/space. Four hundred and forty-five adult patients were enrolled. The median age of the recipients was 51 (range 19 to 71) years old, and 64.9% were males. SSIs were documented in 108 (24.3%) patients. Organ/space SSIs predominated, accounting for 59 (54.6%) patients followed by superficial SSI (n = 47, 43.5%) with only deep infections. Factors predictive of SSIs by multivariate analysis were pancreas cold ischemic time (Odds ratio 1.002, P = 0.11) and simultaneous SKP transplant (as compared with pancreas transplant alone, Odds ratio 2.38, P = 0.003). SSIs were associated with longer duration of hospital stay (P < 0.001). Organ/space SSIs remain a serious and common complication after SKP transplant. Longer pancreas cold ischemic time and simultaneous kidney and pancreas transplantation were the risk factors predictive of SSI. Efforts to improve pancreatic cold ischemic time and optimize perioperative antimicrobial prophylaxis in high-risk patients targeting potential pathogens producing SSIs in SKP transplant patients are warranted. All authors: No reported disclosures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".