Do Preoperative Nasal Antiseptic Swabs Reduce the Rate of Surgical Site Infections After Adult Thoracolumbar Spine Surgery?
Bibliographic record
Abstract
INTRODUCTION: Surgical site infection (SSI) remains a major complication after adult spinal surgery. We investigated whether adding preoperative nasal decontamination by antiseptic swab (skin and nasal antiseptic povidone-iodine, SNA-PI) to our antimicrobial protocol reduces the SSI rate among our patients undergoing thoracolumbar spinal surgery. METHODS: We retrospectively reviewed all adult thoracolumbar spinal surgeries performed between June 2015 and May 2017 at a single hospital. Patients were divided into those who received nasal decontamination (SNA-PI+) and those who did not (SNA-PI-). SSI rates and responsible pathogens were compared between the cohorts. RESULTS: A total of 1,555 surgeries with nasal decontamination (SNA-PI+) and 1,423 surgeries without (SNA-PI-) were included. The SSI rate in the SNA-PI+ group was 13 of 1,555 (0.8%) versus 10 of 1,423 (0.7%) for SNA-PI- group (P = 0.68). The infection rate was the highest among posterior instrumented fusions in the SNA-PI+ group (1.4%). Methicillin-sensitive Staphylococcus aureus was responsible for 70% of infections in the SNA-PI- group and 38% in the SNA-PI+ group (P = 0.13). CONCLUSIONS: Routine nasal antiseptic swab before spine surgery did not affect the overall rate of SSI in thoracolumbar spinal surgeries. The incidence of methicillin-sensitive S aureus was lower in patients who received nasal decontamination (5/1,555, 0.3%) compared with those who did not (7/1,423, 0.5%); however, this result was not statistically significant (P = 0.57).
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".