Antiseptic Irrigation as an Effective Interventional Strategy for Reducing the Risk of Surgical Site Infections
Bibliographic record
Abstract
A surgical site infection (SSI) can occur at several anatomic sites related to a surgical procedure: Superficial or deep incisional or organ/space. The SSIs are the leading cause of health-care-associated infection (HAI) in industrialized Western nations. Patients in whom an SSI develops require longer hospitalization, incur significantly greater treatment costs and reduction in quality of life, and after selective surgical procedures experience higher mortality rates. Effective infection prevention and control requires the concept of the SSI care bundle, which is composed of a defined number of evidence-based interventional strategies, because of the many risk factors that can contribute to the development of an SSI. Intra-operative irrigation has been a mainstay of surgical practice for well over 100 years, but lacks standardization and compelling evidence-based data to validate its efficacy. In an era of antibiotic stewardship, with a widespread prevalence of bacterial resistance to multiple antibiotic agents, there has emerged an interest in using intra-operative antiseptic irrigation to reduce microbial contamination in the surgical site before closure and possibly reduce the need for antibiotic agents. This approach has gained added appeal in an era of biomedical device implantation, especially with the recognition that most, if not all, device-related infections are associated with biofilm formation. This review focuses on the limited, evidence-based rationale for the use of antiseptic agents as an effective risk reduction strategy for prevention of SSIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".