Fire and Ice: Percutaneous Ablative Therapies and Cement Injection in Management of Metastatic Disease of the Spine
Bibliographic record
Abstract
Oncology intervention is actively moving beyond simple bone cement injection. Archimedes taught us that a volume displaces its volume. Where does the tumor we displace with our cement injection go? It is no longer acceptable that we displace tumor into the venous system with our cement injections. We must kill the tumor first. Different image-guided percutaneous techniques can be used for treatment in patients with primary or secondary bone tumors. Curative ablation can be applied for the treatment of specific benign or in selected cases of malignant localized spinal tumors. Pain palliation therapy of primary and secondary bone tumors can be achieved with safe, fast, effective, and tolerable percutaneous methods. Ablation (chemical, thermal, mechanical), cavitation (radiofrequency ionization), and consolidation (cementoplasty) techniques can be used separately or in combination. Each technique has its indications, with both advantages and drawbacks. To prevent pathological fractures, a consolidation is necessary. In spinal or acetabular tumors, a percutaneous cementoplasty should be associated with cryoablation to avoid a compression fracture. The cement is injected after complete thawing of the ice ball or the day after the cryotherapy. A syndrome of multiorgan failure, severe coagulopathy, and disseminated intravascular coagulation following hepatic cryoablation has been described and is referred to as the cryoshock phenomenon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".