Percutaneous cryoablation in the management of spinal metastases: a comprehensive systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Minimally invasive techniques such as vertebroplasty, kyphoplasty, radiofrequency ablation, and stereotactic body radiotherapy have been widely used to manage spinal metastases. Among these, percutaneous cryoablation (PCA) has emerged as a promising option for local tumor control and pain management, offering targeted treatment with minimal damage to surrounding tissues. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of PCA for spinal metastases. METHODS: A systematic review was conducted using PubMed and Embase databases to identify studies that reported outcomes of PCA for spinal metastases. The reported radiologic, clinical, and complication outcomes were then combined and analyzed using meta-analytic methods including the calculation of pooled means and proportions, subgroup analysis, and meta-regression. RESULTS: Eleven studies, including 229 patients, met inclusion criteria and were analyzed. Patients had a mean age of 61.8 years, with 60.6% being female. Breast (18.6%), lung (16.0%), and thyroid (8.0%) were the most common primary cancer sites. PCA was primarily conducted under general anesthesia (47.5%) and with CT/MRI guidance (93.9%). Local tumor control was achieved in 70.5% of cases over a mean follow-up of 12.6 months. Pain severity significantly decreased postoperatively, with a mean reduction of 4.5 points (P < 0.0001). Major and minor complication rates were 2.0% and 4.8%, respectively. CONCLUSIONS: PCA is an effective alternative treatment for spinal metastases, offering pain relief and local tumor control with low complication rates in appropriately selected patients. However, tumor location and patient age may influence treatment outcomes, underscoring the need for individualized treatment planning.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".