The era of stereotactic body radiotherapy for spinal metastases and the multidisciplinary management of complex cases
Bibliographic record
Abstract
Spinal metastases are increasingly becoming a focus of attention with respect to treating with locally "ablative" intent, as opposed to locally "palliative" intent. This is due to increasing survival rates among patients with metastatic disease, early detection as a result of increasing availability of spinal MRI, the recognition of the oligometastatic state as a distinct sub-group of favorable metastatic patients and the advent of stereotactic body radiotherapy (SBRT). Although conventionally fractionated radiation therapy has been utilized for decades, the rates of complete pain relief and local control for complex tumors are sub-optimal. SBRT has the advantage of delivering high total doses in few fractions (typically, 24 Gy in 1 or 2 fractions to 30-45 Gy in 5 fractions) that can be considered "ablative". With mature clinical experience emerging among early adopters, we are realizing beyond efficacy the limitations of spine SBRT. In particular, toxicities such as vertebral compression fracture, and epidural disease progression as the most common pattern of local tumor progression. As a result, the multidisciplinary evaluation of cases prior to SBRT is emphasized with the intent to identify patients who could benefit from surgical stabilization or down-staging of epidural disease. The purpose of this review is to provide an overview of the current literature with respect to outcomes, technical details for safe delivery, patient selection criteria, common and uncommon side effects of therapy, and the increasing use of minimally invasive surgical techniques that can improve both safety and local control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".