Management of deep surgical site infections of the spine: a Canadian nationwide survey
Bibliographic record
Abstract
Background: Deep surgical site infections after spinal instrumentation represent a significant source of patient morbidity and poorer outcomes. Given lack of evidence or guidelines on the variety of procedural options in the management of deep spine surgical site infections, the purpose of this survey was to document and investigate the use of these techniques across Canada. Methods: A 34-question survey evaluating surgical techniques for irrigation and debridement in postoperative thoracolumbar infection was distributed to Canadian adult spine surgeons. Results were analyzed qualitatively, and comparisons by specialty, years of training, and number of cases were completed using Fischer's exact tests. We defined consensus as >70% agreement. Results: We received 53 responses (62% response rate) from a comprehensive sample of Canadian adult spine surgeons. There was a consensus to retain hardware (80%) and interbody implants (93%) in acute infection, to retain interbody implants in chronic/recurrent infection (71%), and application of topical antibiotics in recurrent infection (85%). There was consensus on the use of absorbable suture to close fascia in acute (83%) and chronic (87%) infection. Eighty-five percent of surgeons used nonabsorbable materials such as Nylon or staples for skin closure in chronic infection, however, there was no consensus in acute infection. Surgeons varied significantly in type, volume and pressure of fluids, adjuvant solvents, graft management, use of topical antibiotics acutely, and the use of negative pressure wound therapy. Partial hardware exchange was controversial. Additionally, specialty or surgeon experience had no impact on management strategy. Conclusions: This survey demonstrates significant heterogeneity amongst Canadian adult spine surgeons regarding key steps in the surgical management of deep instrumented spine infection, concordant with scarce literature addressing these steps.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".