Exploring the Efficacy of Combining Radiofrequency Thermal Ablation or Microwave Ablation with Vertebroplasty for Pain Control and Disease Management in Metastatic Bone Disease—A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Interventional radiology techniques have become pivotal in recent years in managing metastatic bone disease, which frequently results in skeletal complications such as fractures and severe pain. Thermoablative methods like radiofrequency ablation (RFA) and microwave ablation (MWA), when combined with vertebroplasty (VP), are proving increasingly beneficial for these patients. METHODS: The search was independently conducted by two radiologists on MEDLINE databases, using specified strings up to April 2024. Methodological quality was assessed using PRISMA guidelines. Studies meeting inclusion criteria investigated thermoablation techniques (RFA and/or MWA) combined with VP, focusing on pain management and disease control outcomes in adults. RESULTS: Among 147 results, 42 articles met the criteria, with varied prospective and retrospective designs and sample sizes averaging 49 patients, predominantly involving RFA (30 studies), MWA (11 studies), and one comparative study. Our review highlights significant pain reduction, effective local tumor control, and favorable safety of combined RFA or MWA with VP, supporting its potential in managing vertebral pathologies and warranting further clinical integration. CONCLUSIONS: The combined treatment of RFA/MWA with VP demonstrates significant pain reduction and local tumor control, with a rapid onset of analgesic effect. These findings support its crucial role in clinical practice for managing vertebral metastases.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".