Intraoperative surgical site infection control and prevention: a position paper and future addendum to WSES intra-abdominal infections guidelines
Bibliographic record
Abstract
BACKGROUND: Surgical site infections (SSI) represent a considerable burden for healthcare systems. They are largely preventable and multiple interventions have been proposed over past years in an attempt to prevent SSI. We aim to provide a position paper on Operative Room (OR) prevention of SSI in patients presenting with intra-abdominal infection to be considered a future addendum to the well-known World Society of Emergency Surgery (WSES) Guidelines on the management of intra-abdominal infections. METHODS: The literature was searched for focused publications on SSI until March 2019. Critical analysis and grading of the literature has been performed by a working group of experts; the literature review and the statements were evaluated by a Steering Committee of the WSES. RESULTS: Wound protectors and antibacterial sutures seem to have effective roles to prevent SSI in intra-abdominal infections. The application of negative-pressure wound therapy in preventing SSI can be useful in reducing postoperative wound complications. It is important to pursue normothermia with the available resources in the intraoperative period to decrease SSI rate. The optimal knowledge of the pharmacokinetic/pharmacodynamic characteristics of antibiotics helps to decide when additional intraoperative antibiotic doses should be administered in patients with intra-abdominal infections undergoing emergency surgery to prevent SSI. CONCLUSIONS: The current position paper offers an extensive overview of the available evidence regarding surgical site infection control and prevention in patients having intra-abdominal infections.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".