Optimization of Tumor Volume Reduction and Cement Augmentation in Percutaneous Vertebroplasty for Prophylactic Treatment of Spinal Metastases
Bibliographic record
Abstract
OBJECTIVE: Spinal metastatic disease occurs in up to one-third of all cancer patients. Metastasis can lead to vertebral burst fracture and consequent neurologic compromise. Percutaneous vertebroplasty (PV) is a minimally invasive procedure aimed at restoring vertebral stability by augmentation of weakened vertebrae with bone cement. PV is associated with a complication rate of 10% in treating vertebral metastases. Tumor ablation before cement injection has been suggested to improve PV outcome in the metastatic spine. The objectives of this study were to quantify the effects of volumetric tumor reduction and cement augmentation in the metastatic spine and to develop a protocol for recommended cement volume to achieve sufficient restoration of intact (nonpathologic) vertebral body stability. METHODS: A biphasic parametric finite element model of an L1 spinal motion segment was developed and validated against previously collected experimental data. Using this model, 12 scenarios were simulated to represent tumor volume reductions of up to 60% and cement augmentation from 1 to 8 mL. CONCLUSIONS: Restoration of intact vertebral stability is possible in metastatic vertebrae after 30% tumor ablation and 1 to 2 mL bone cement augmentation. A protocol was developed on the basis of the findings of this study suggesting recommended cement volume for injection as a function of remaining tumor volume after ablation. These findings may motivate refined methods of prophylactic treatment of metastatic vertebrae.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".