Strategies to Mitigate Toxicities From Stereotactic Body Radiation Therapy for Spine Metastases
Bibliographic record
Abstract
Improvements in systemic therapy are translating into more patients living longer with metastatic disease. Bone is the most common site of metastasis, where spinal lesions can result in significant pain impacting quality of life and possible neurological dysfunction resulting in a decline in performance status. Stereotactic body radiation therapy (SBRT) of the spine has emerged as a promising technique to provide durable local control, palliation of symptoms, control of oligoprogressive sites of disease, and possibly augment the immune response. SBRT achieves this by delivering highly conformal radiation therapy to allow for dose escalation due to a steep dose gradient from the planning target volume to nearby critical organs at risk. In our review, we provide an in-depth review and expert commentary regarding seminal literature that defined clinically meaningful toxicity endpoints with actionable dosimetric limits and/or clinical management strategies to mitigate toxicity potentially attributable to SBRT of the spine. We placed a spotlight on radiation myelopathy (de novo, reirradiation after conventional external beam radiation therapy or salvage after an initial course of spinal SBRT), plexopathy, vertebral compression fracture, pain flare, esophageal toxicity, myositis, and safety regarding combination with concurrent targeted or immune therapies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".