The Efficacy of Computed Tomography-Guided Percutaneous Spine Biopsies in Determining a Causative Organism in Cases of Suspected Infection: A Systematic Review
Bibliographic record
Abstract
PURPOSE: In suspected spondylodiscitis and vertebral osteomyelitis, computed tomography (CT)-guided biopsies are often performed to determine a causative organism and guide antimicrobial therapy. The aim of this study is to determine the diagnostic culture yield of CT-guided biopsies performed in cases of suspected spinal infections. METHODS: A literature search of PubMed and MEDLINE up to April 2017 was performed for keywords "CT guided vertebral biopsy infection," "CT-guided spine biopsy infection," "CT guided spine biopsy yield," and "CT guided vertebral biopsy yield." Inclusion criteria primarily consisted of studies exclusively using CT-guided biopsies in cases of suspected infectious lesions only. After study selection, published articles were analysed to determine diagnostic culture yield. Descriptive statistics were applied. RESULTS: 220 search results were screened; 11 met our inclusion criteria and were reviewed. In total, 647 biopsies of suspected infectious spinal lesions were performed. Positive cultures were obtained in 241 cases. Upon excluding one paper's skewed results, the net pooled results culture yield was 33%. Several cultures grew multiple organisms, leading to a total of 244 species identified. Most common isolated organisms include Staphylococcus aureus (n = 83), coagulase-negative Staphylococcus (n = 45), and Mycobacteria (n = 38). CONCLUSIONS: The diagnostic culture yield of CT-guided biopsies in cases of suspected spinal infection is 33%. In the majority of cases, a causative organism is not identified. This suggests that improvements can be made in biopsy technique and specimen transfer to optimize culture yield and increase the clinical value of the procedure.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".