Optimization of Tumor Volume Reduction and Cement Augmentation in Percutaneous Vertebroplasty for Prophylactic Treatment of Spinal Metastases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it