Surgical-Site Infection Following Spinal Fusion: A Case-Control Study in a Children's Hospital
Bibliographic record
Abstract
OBJECTIVES: To determine the rates of surgical-site infections (SSIs) after spinal surgery and to identify the risk factors associated with infection. DESIGN: SSIs had been identified by active prospective surveillance. A case-control study to identify risk factors was performed retrospectively. SETTING: University-associated, tertiary-care pediatric hospital. PATIENTS: All patients who underwent spinal surgery between 1994 and 1998. Cases were all patients who developed an SSI after spinal surgery. Controls were patients who did not develop an SSI, matched with the cases for the presence or absence of myelodysplasia and for the surgery date closest to that of the case. RESULTS: There were 10 infections following 125 posterior spinal fusions, 4 infections after 50 combined anterior-posterior fusions, and none after 95 other operations. The infection rate was higher in patients with myelodysplasia (32 per 100 operations) than in other patients (3.4 per 100 operations; relative risk = 9.45; P < .001). Gram-negative organisms were more common in early infections and Staphylococcus aureus in later infections. Most infections occurred in fusion involving sacral vertebrae (odds ratio [OR] = 12.0; P = .019). Antibiotic prophylaxis was more frequently suboptimal in cases than in controls (OR = 5.5; P = .034). Five patients required removal of instrumentation and 4 others required surgical debridement. CONCLUSIONS: Patients with myelodysplasia are at a higher risk for SSIs after spinal fusion. Optimal antibiotic prophylaxis may reduce the risk of infection, especially in high-risk patients such as those with myelodysplasia or those undergoing fusion involving the sacral area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".