Prevention of Surgical Site Infection in Spine Surgery
Bibliographic record
Abstract
BACKGROUND: Spine surgery is complicated by an incidence of 1% to 9% of surgical site infection (SSI). The most common organisms are gram-positive bacteria and are endogenous, that is are brought to the hospital by the patient. Efforts to improve safety have been focused on reducing SSI using a bundle approach. The bundle approach applies many quality improvement efforts and has been shown to reduce SSI in other surgical procedures. OBJECTIVE: To provide a narrative review of practical solutions to reduce SSI in spine surgery. METHODS: Literature review and synthesis to identify methods that can be used to prevent SSI. RESULTS: SSI prevention starts with proper patient selection and optimization of medical conditions, particularly reducing smoking and glycemic control. Screening for staphylococcus organisms and subsequent decolonization is a promising method to reduce endogenous bacterial burden. Preoperative warming of patients and timely administration of antibiotics are critical to prevent SSI. Skin preparation using chlorhexidine and alcohol solutions are recommended. Meticulous surgical technique and maintenance of sterile techniques should always be performed. Postoperatively, traditional methods of tissue oxygenation and glycemic control remain essential. Newer wound care methods such as silver impregnation dressing and wound-assisted vacuum dressing are encouraging but need further investigation. CONCLUSION: Significant reduction of SSIs is possible, but requires a systems approach involving all stakeholders. There are many simple and low-cost components that can be adjusted to reduce SSIs. Systematic efforts including understanding of pathophysiology, prevention strategies, and system-wide quality improvement programs demonstrate significant reduction of SSI.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".