Surgical Site Infection in Spine Surgery: Who Is at Risk?
Bibliographic record
Abstract
STUDY DESIGN: Retrospective literature review of spine surgical site infection (SSI). OBJECTIVE: To perform a review of SSI risk factors and more specifically, categorize them into patient and surgical factors. METHODS: A review of published literature on SSI risk factors in adult spine surgery was performed. We included studies that reported risk factors for SSI in adult spinal surgery. Excluded are pediatric patient populations, systematic reviews, and meta-analyses. Overall, we identified 72 cohort studies, 1 controlled-cohort study, 1 matched-cohort study, 1 matched-paired cohort study, 12 case-controlled studies (CCS), 6 case series, and 1 cross-sectional study. RESULTS: ), subcutaneous fat thickness, multiple medical comorbidities, current smoker, and malnutrition were associated with SSI. Surgical associated factors-preoperative radiation/postoperative blood transfusion, combined anterior/posterior approach, surgical invasiveness, or levels of instrumentation were associated with increased SSI. There is mixed evidence of age, duration of surgery, surgical team, intraoperative blood loss, dural tear, and urinary tract infection/urinary catheter in association with SSI. CONCLUSION: SSIs are associated with many risk factors that can be patient or surgically related. Our review was able to identify important modifiable and nonmodifiable risk factors that can be essential in surgical planning and discussion with patients.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".