Is Surgical Case Order Associated With Increased Infection Rate After Spine Surgery?
Bibliographic record
Abstract
STUDY DESIGN: Retrospective database review. OBJECTIVE: To determine whether surgical site infections are associated with case order in spinal surgery. SUMMARY OF BACKGROUND DATA: Postoperative wound infection is the most common complication after spinal surgery, with incidence varying from 0.5% to 20%. The addition of instrumentation, use of preoperative prophylactic antibiotics, length of procedure, and intraoperative blood loss have all been found to influence infection rate. No previous study has attempted to correlate case order with infection risk after surgery. METHODS: A total of 6666 spine surgery cases occurring between January 2005 and December 2009 were studied. Subjects were classified into 2 categories: fusion and decompression. Case order was determined, with each procedure labeled 1 to 5 depending on the number of previous cases in the room. Variables such as the American Society of Anesthesiologists score, number of operative levels, wound class, age, sex, and length of surgery were also tracked. A step-down binary regression was used to analyze each variable as a potential risk factor for infection. RESULTS: Decompression cases had a 2.4% incidence of infection. Longer surgical time and higher case order were found to be significant risk factors for lumbar decompressions. Fusion cases had a 3.5% incidence of infection. Posterior approach and revision cases were significant risk factors for infection in cervical cases. For lumbar fusion cases, longer surgical time, higher American Society of Anesthesiologists score, and older age were all significant risk factors for infection. CONCLUSION: Decompressive procedures performed later in the day carry a higher risk for postoperative infection. No similar trend was shown for fusion procedures. Our results identify potential modifiable risk factors contributing to infection rates in spinal procedures. Specific risk factors, although not defined in this study, might be related to contamination of the operating room, cross-contamination between health care providers during the course of the day, use of flash sterilization, and mid-day shift changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.020 | 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 teacher head, 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".