Preoperative Interventions for the Prevention of Surgical Site Infections: A Review of Guidelines [Internet]
Bibliographic record
Abstract
Defined as postoperative infections of an incision, organ, or space, surgical site infections (SSIs) are the most common health care-related infections. Occurring in up to 5% of all surgeries, SSIs affect approximately 26,000 to 65,000 Canadian patients annually. Due to increased hospital stays and readmission rates, SSIs cost the health care system between $350,000 to $1 million each year. To help reduce morbidity, extended hospitalization, and death, infection control measures have been implemented in surgical settings.Surgical infection control measures include staff precautions such as practicing hand hygiene and using barrier devices, and patient-specific perioperative infection control interventions that may include nasal decolonization for Staphylococcus aureus (S. aureus), preoperative washing, skin antisepsis, hair removal, glucose control, bowel preparation, and antibiotic prophylaxis. It has been shown that almost half of SSIs may be prevented by applying evidence-based strategies. SSI prevention measures can be bundled to promote staff and patient adherence, but there is a lack of consensus regarding the appropriate components of an infection control bundle.This report is an upgrade from a previous CADTH Reference List report published in 2020, and includes one of the research questions from that report. The aim of the current report is to summarize and critically appraise the relevant evidence-based guidelines identified in the previous report regarding preoperative interventions for the prevention of SSIs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".