Electrochemotherapy Is Effective in the Treatment of Bone Metastases
Bibliographic record
Abstract
Bone metastases induce pain, risk of fracture, and neural compression, and reduced mobility and quality of life. Electrochemotherapy (ECT) is a minimally invasive local treatment based on a high-voltage electric pulse combined with an anticancer drug. Preclinical and clinical studies have supported the use of ECT in patients with metastatic bone disease, demonstrating that it does not damage the mineral structure of the bone and its regenerative capacity, and that is feasible and efficient for the treatment of bone metastases. Since 2009, 88 patients with bone metastasis have received ECT at the Rizzoli Institute. 2014 saw the start of a registry of patients with bone metastases treated with ECT, whose data are recorded in a shared database. We share the Rizzoli Institute experience of 38 patients treated with ECT for a bone metastasis, excluding patients not included in the registry (before 2014) and those treated with bone fixation. Mean follow-up was 2 months (1-52). Response to treatment using RECIST criteria was 29% objective responses, 59% stable disease, and 16% progressive disease. Using PERCIST, the response was 36% OR, 14% SD, and 50% PD with no significant differences between the two criteria. A significant decrease in pain and better quality of life was observed at FU.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".