Reduction of Surgical Site Complications in Post-Operative Kidney Transplant Patients
Bibliographic record
Abstract
Surgical site infections (SSI) and complications lead to significant morbidity, increased antibiotic use and length of hospital stay, additional costs, hospital readmissions, and a decline in patients’ quality of life. SSIs account for 3.2 billion dollars in cost per year in acute care hospitals. Those undergoing kidney transplantation are also at a higher risk for SSIs due to comorbidities and immunosuppression medication. Nursing staff in a transplant center in a large midwestern teaching hospital identified factors that could decrease their rate of surgical site infections and complications. The purpose of this quality improvement project was to analyze the surgical site complication rate for persons who received kidney transplants in 2021 and to reduce patient readmission for wound complications following kidney transplants. The Ottawa Model for Health Care Research was the framework used to guide implementation of this quality improvement project. An inpatient to outpatient wound status handoff tool was created and identification of those in need of more teaching on wound care was assessed. The rate of surgical site complications in 2021 at a large midwestern teaching hospital was found to be 36%. These included surgical site infections, fluid collections, and hematomas/seromas. Formal interviews with staff identified the need for extra time and modification of education resources to be provided with those with a language barrier and lower health literacy. Staff also identified patient involvement in surgical site care early in the post-transplant period crucial due to the large amount of education provided to transplant patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".