Achieving Zero Coronary Artery Bypass Graft Surgical Site Infections for over Four Years: Our Experience Utilizing Bundle Elements, Education, and Audits
Bibliographic record
Abstract
Background: Surgical site infections (SSI) are expensive and potentially deadly infections; however, with evidence-based infection prevention techniques many can be prevented. The purpose of this quality improvement project is to describe our hospital’s experience achieving zero coronary artery bypass graft (CABG) deep incisional and organ/space SSI incidences from October 2016-July 2021. Methods: To prevent CABG deep incisional and organ/space SSI incidences our Infection Prevention and Epidemiology Department along with the Cardiothoracic and Vascular Surgery Department established SSI prevention bundle elements, continuous education, and monthly audits. Results: From quarter one of 2015 through quarter three of 2016 there were three deep incisional or organ/space SSI cases out of 317 CABG procedures. From quarter four of 2016 through quarter two of 2021 there have been 625 CABG procedures, zero of which developed into deep incisional or organ/space SSI incidences. CABG SSI prevention bundle element compliance ranged from 88.2% to 99.6% and operating room environment of care compliance was 94%. Conclusion: Our results show overall improvements in our quarterly CABG SSI SIR from quarter one of 2015 to quarter two of 2021. This experience demonstrates the importance of complying with SSI prevention bundle elements, education, and auditing in reducing and maintaining zero CABG deep incisional and organ/space incidences for over four years.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".