Strategies to Prevent Surgical Site Infections in Acute Care Hospitals
Bibliographic record
Abstract
Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections. The intent of this document is to highlight practical recommendations in a concise format designed to assist acute care hospitals to implement and prioritize their surgical site infection (SSI) prevention efforts. Refer to the Society for Healthcare Epidemiology of America/Infectious Diseases Society of America “Compendium of Strategies to Prevent Healthcare-Associated Infections” Executive Summary and Introduction and accompanying editorial for additional discussion. 1. Burden of SSIs as complications in acute care facilities. a. SSIs occur in 2%-5% of patients undergoing inpatient surgery in the United States. b. Approximately 500,000 SSIs occur each year. 2. Outcomes associated with SSI a. Each SSI is associated with approximately 7-10 additional postoperative hospital days. b. Patients with an SSI have a 2-11 times higher risk of death, compared with operative patients without an SSI. i. Seventy-seven percent of deaths among patients with SSI are direcdy attributable to SSI. c. Attributable costs of SSI vary, depending on the type of operative procedure and the type of infecting pathogen; published estimates range from $3,000 to $29,000. i. SSIs are believed to account for up to $10 billion annually in healthcare expenditures. 1. Definitions a. The Centers for Disease Control and Prevention National Nosocomial Infections Surveillance System and the National Healthcare Safety Network definitions for SSI are widely used. b. SSIs are classified as follows (Figure): i. Superficial incisional (involving only skin or subcutaneous tissue of the incision) ii. Deep incisional (involving fascia and/or muscular layers) iii. Organ/space
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".