Enhanced Recovery After Surgery Cardiac Society turnkey order set for surgical-site infection prevention: Proceedings from the American Association for Thoracic Surgery ERAS Conclave 2023
Bibliographic record
Abstract
OBJECTIVES: Surgical-site infections (SSIs) after cardiac surgery increase morbidity and mortality, consume health care resources, impair recovery, and diminish patients' quality of life. Numerous guidelines and expert consensus documents have been published to address the prevention and management of SSIs. Our objective is to integrate these documents into an order set that will facilitate the adoption and implementation of evidence-based best practices for preventing and managing SSIs after cardiac surgery. METHODS: Subject matter experts were consulted to translate existing guidelines and literature into a sample turnkey order set for SSI reduction. Orders derived from consistent class I, IIA, or equivalent recommendations across referenced guidelines and consensus manuscripts appear in the turnkey order set in bold type. Selected orders that were inconsistent class I or IIA, class IIB or otherwise supported by published evidence, were also included in italicized type. RESULTS: Preventative care begins with the preoperative identification of both modifiable and nonmodifiable SSI risks by health care providers. Assessment tools can be used to assist in identifying patients at a high risk of SSI. Preoperative recommendations include screening for and treating Staphylococcus aureus nasal carriage. Intraoperatively, tailored prophylactic intravenous antibiotics and maintaining blood glucose levels below 180 mg/dL are essential elements. Postoperative care includes maintaining normothermia, glucose control and patient engagement. CONCLUSIONS: Despite the well-documented advantages of a multidisciplinary care pathway for SSI in cardiac surgery, there are inconsistencies in its adoption and implementation. This article provides an order set that incorporates recommendations from existing guidelines to prevent SSI in the cardiac surgical 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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".