Vertebral Compression Fracture After Spine Stereotactic Body Radiation Therapy: A Review of the Pathophysiology and Risk Factors
Bibliographic record
Abstract
BACKGROUND: Vertebral compression fracture (VCF) is a challenging and not infrequent complication observed following spine stereotactic body radiation therapy (SBRT). OBJECTIVE: To summarize the data from the multiple studies that have been published, addressing the risk and predictive factors for VCF post-SBRT. METHODS: A systematic literature review was conducted. Studies were selected if they specifically addressed risk factors for post-SBRT VCF in their analyses. RESULTS: A total of 11 studies were identified, reporting both the risk of VCF post-SBRT and an analysis of risk factors based on univariate and multivariate analysis. A total of 2911 spinal segments were treated with a crude VCF rate of 13.9%. The most frequently identified risk factors on multivariate analysis were: lytic disease (hazard ratio [HR] range, 2.76-12.2), baseline VCF prior to SBRT (HR range, 1.69-9.25), higher dose per fraction SBRT (HR range, 5.03-6.82), spinal deformity (HR range, 2.99-11.1), older age (HR range, 2.15-5.67), and more than 40% to 50% of vertebral body involved by tumor (HR range, 3.9-4.46). In the 9 studies that specifically reported on the use of post-SBRT surgical procedures, 37% of VCF had undergone an intervention (range, 11%-60%). CONCLUSION: VCF is an important adverse effect following SBRT. Risk factors have been identified to guide the selection of high-risk patients. Evidence-based algorithms with respect to patient selection and intervention 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 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".