Surgical Site Infection Management following Spinal Instrumentation Surgery: Implant Removal vs. Implant Retention: an Updated Systematical Review
Bibliographic record
Abstract
Background: The number of lumbar spine surgery increased in recent years. Spinal instrumentation surgery was an integral component in the treatment of spinal pathologies, which can cause surgical site infection (SSI). Surgical site infections (SSIs) are the leading cause of mortality and morbidity after spinal instrumentation surgery. The management of SSI was implant retention and removal is still unclear. Objective: The objective of this literature is to systematically review the implant removal and retention method for SSI management after spinal instrumentation surgery. Methods: We searched in PubMed and ScienceDirect for cohort and randomized control trial studies in English, published between 2002 and 2022, which had data on patients with spinal instrumentation surgery. The underlying disease, comorbidities, common bacteria, type of infection, the onset of infection, implant removal, and retention percentage and recommendation were analyzed. Bias analysis using Newcastle-Ottawa Quality Assessment. Results: We included 15 studies with a total sample were 2.584 with an average of age 15 to 66 years old. The most common organism detected were S. Aureus, MRSA, and S. Epidermis. The most common surgical procedure indications were degenerative followed by scoliosis. Implant removal and retention rate were 0-100% and 0-90,32% respectively. Implant removal is more frequently used in patients after spinal instrumentation surgery than the implant retention method. Conclusion: Implant retention can be performed in case of SSI is < 3 months after surgery. Implant removal is recommended if the incidence of SSI is > 3 months. Empirical antibiotics therapy is necessary to reduce the possibility of implant removal after debridement. Further studies on the effect of implant removal and retention in patients on infection recurrence, pain, and quality of life of patients are needed.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".