Surgical site infection rates in dialysis patients undergoing endovascular procedures
Bibliographic record
Abstract
A surgical site infection (SSI) is an infection related to surgery that develops within 30 days after an operation or within 1 year of implant placement. Postoperative SSIs are the most common health-care-associated infections, occurring in up to 5% of surgical patients. Endovascular surgical procedures related to vascular access are common in the dialysis population and may cause SSIs. A large outpatient vascular access system developed and implemented a surveillance program to measure and monitor SSIs in their population. The health-care surveillance system extended to 76 ambulatory care centers across the United States and Puerto Rico. Based on a recorded 92,880 patient encounters, the surveillance system tabulated 12,541 valid patient survey responses documenting self-reported symptoms of infection within a 30-day postoperative period. The SSI rate was tabulated based on the presence of two or more specified indicators of infection: antibiotics, pus, dehiscence, pain, warmth, and swelling. Patients undergoing interventional procedures received surveys at discharge. Data were collected and analyzed using SPSS software. Survey analysis indicated a less than 3% superficial incisional SSI rate in hemodialysis patients undergoing endovascular procedures. The SSI rate for clean wound procedures is generally 2% or less. These data indicate that dialysis patients undergoing interventional procedures in vascular access centers may have a slightly greater risk of developing SSIs due to the presence of additional risk factors including obesity, diabetes, and age. This study was limited by a set of loose diagnostic criteria self-reported by patients, which may have overestimated the prevalence of infection. SSIs are a serious medical problem associated with increased morbidity and mortality and increased medical care costs. All providers should consider an active surveillance program following endovascular procedures given the comorbidities associated with the dialysis population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".