Deep wound infection following pediatric scoliosis surgery: incidence and analysis of risk factors
Bibliographic record
Abstract
BACKGROUND: Deep wound infection after spinal surgery is a severe complication that often requires prolonged medical and surgical management. It can compromise the outcome of the deformity correction, especially in patients requiring surgical intervention with subsequent removal of implants. Ascertaining the incidence and risk factors leading to infection may help to prevent this problem. METHODS: We reviewed the hospital charts of all patients who underwent spinal deformity correction from 1996 to 2005. RESULTS: In all, 227 patients were identified (139 idiopathic, 57 neuromuscular, 8 syndromic, 6 congenital, 17 other); 191 patients were treated with posterior instrumentation and fusion, 11 with anterior-only procedures and 24 with combined anterior and posterior procedures. Final follow-up ranged from 1 to 9.5 years. Infection developed in 14 patients. The overall incidence of infection was 6.2%. Drainage and back pain were the most common presenting symptoms. The incidence of infection was higher among patients with nonidiopathic diagnoses (risk ratio [RR] 8.65, p < 0.001). Use of allograft bone was associated with a higher rate of infection (RR 9.66, p < 0.001) even when stratified by diagnosis (nonidiopathic diagnoses, RR 7.6, p = 0.012). Higher volume of instrumentation was also a risk factor for infection (p = 0.022). Coagulase-negative Staphyloccocus was the most commonly identified organism, followed by Propionibacterium acnes and Pseudomonas. CONCLUSION: Development of infection following scoliosis surgery was found to be associated with several risk factors, including a nonidiopathic diagnosis, the use of allograft and a higher volume of instrumentation. Preventative measures addressing these factors may decrease the rate of infection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".