Neuroimaging and Stereotactic Body Radiation Therapy (SBRT) for Spine Metastasis
Bibliographic record
Abstract
Historically, management options for spinal metastases include surgery for stabilization and decompression and/or external beam radiation therapy (EBRT). EBRT is palliative in nature, as it lacks accurate targeting such that the prescribed radiation doses must be limited in order to maintain safety. Modern advancement in imaging and radiotherapy technology have facilitated the development of stereotactic body radiation therapy (SBRT), which provides increased targeted precision for radiation delivery to tumors resulting in lower overall toxicity, particularly to regional structures such as the spinal cord and esophagus, while delivering higher, more effective, and radically ablative radiation doses.Over the past decade, SBRT has been increasingly utilized as a method of treating spinal metastases either as the primary modality or following surgical intervention in both de novo and reirradiation setting. Numerous studies suggest that SBRT is associated with an 80% to 90% rate of 1-year local control across clinical scenarios. For example, studies of SBRT as the primary treatment modality suggest long-term local control rate of 80% to 95% for spinal metastases. Similarly, SBRT in the adjuvant setting following surgery is associated with local control rates ranging from 70% to 100%. Furthermore, because SBRT allows for lower dose to the spinal cord, it has also been used in patients who have had prior radiation therapy, with studies showing 66% to 93% local control in this scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.005 | 0.002 |
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".