The evolution and rise of stereotactic body radiotherapy (SBRT) for spinal metastases
Bibliographic record
Abstract
INTRODUCTION: Owing to improvements in clinical care and systemic therapy, more patients are being diagnosed with, and living longer with, spinal metastases (SM). In parallel, tremendous technological progress has been made in the field of radiation oncology. Advances in both software and hardware are able to integrate three- (and four-) dimensional body imaging with spatially accurate treatment delivery methods. This leads to improved efficacy, shortened treatment schedule, and potentially reduced treatment-related toxicity. Areas covered: In this review, we will look at the progress made by stereotactic body radiotherapy (SBRT) in the management of SM. We will review the technological factors which have enabled the widespread use of SBRT. The efficacy of SBRT, in various clinical scenarios, and associated toxicities will be reviewed. Lastly, we will discuss about patient selection and provide a five-year roadmap. Expert commentary: Spine SBRT is a safe and efficacious treatment option. Practice guidelines recommend the use of SBRT in oligometastatic patients especially those with radio-resistant cancer types, and in scenarios involving re-irradiation. SBRT offers patients dose-intensification over a short schedule which may allow less time off systemic therapy. The results of the phase III trials are eagerly awaited.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".