Radiofrequency Ablation for the Palliative Treatment of Bone Metastases: Outcomes from the Multicenter OsteoCool Tumor Ablation Post-Market Study (OPuS One Study) in 100 Patients
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of radiofrequency (RF) ablation as measured by change in worst pain score from baseline to 3 mo after RF ablation for the palliative treatment of painful bone metastases. MATERIALS AND METHODS: One hundred patients (mean age, 64.6 y) underwent RF ablation for metastatic bone disease and were followed up to 6 mo. Subjects' pain and quality of life were measured before RF ablation and postoperatively by using the Brief Pain Index and European Quality of Life questionnaires. Opioid agent use and device-, procedure-, and/or therapy-related adverse events (AEs) were collected. RESULTS: Eighty-seven patients were treated for tumors involving the thoracolumbar spine and 13 for tumors located in the pelvis and/or sacrum. All ablations were technically successful, and 97% were followed by cementoplasty. Mean worst pain score decreased from 8.2 ± 1.7 at baseline to 3.5 ± 3.2 at 6 mo (n = 22; P < 0.0001 for all visits). Subjects experienced significant improvement for all visits in average pain (P < .0001), pain interference (P < .0001), and quality of life (P < .003). Four AEs were reported, of which 2 resulted in hospitalization for pneumonia and respiratory failure. All 30 deaths reported during the study were attributed to the underlying malignancy and not related to the study procedure. CONCLUSIONS: Results from this study show rapid (within 3 d) and statistically significant pain improvement with sustained long-term relief through 6 mo in patients treated with RF ablation for metastatic bone disease.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".