Stereotactic Body Radiotherapy for Spinal Metastases at the Extreme Ends of the Spine: Imaging-Based Outcomes for Cervical and Sacral Metastases
Bibliographic record
Abstract
BACKGROUND: The unique anatomy and biomechanical features of the cervical spine and sacrum may impact treatment outcomes following spine stereotactic body radiotherapy (SBRT). Current data for spine metastases are not specific for these locations. OBJECTIVE: To report imaging-based SBRT outcomes to cervical and sacral metastases. METHODS: We retrospectively reviewed our prospective spine SBRT database for cervical and sacral metastases. Patients were followed at 2- to 3-mo intervals with a clinical visit and full spine magnetic resonance imaging (MRI) and we report overall survival (OS), vertebral compression fracture (VCF), and MR imaging-based local control (LC) rates. RESULTS: Fifty-two patients and 93 treated spinal segments were identified. Fifty-six segments were within the cervical spine and 37 within the sacrum, the median follow-up was 14.4 and 19.5 mo, and the median total dose/number of fractions was 24 Gy/2, respectively. Cumulative LC at 1 and 2 yr were 94.5% and 92.7% for the cervical cohort, and 86.5% and 78.7% in the sacral cohort, respectively. Lack of posterior spinal element involvement in the cervical spine (P < .0001) and absence of epidural disease (hazard ratio 0.275, 95% confidence interval 0.076-0.989, P = .048) in the sacral cohort predicted LC. Median OS was 16.3 and 28.5 mo in the cervical spine and sacrum cohorts, respectively. Two cases of sacral VCF, 1 brachial plexopathy, and 1 lumbar-sacral plexopathy were observed. CONCLUSION: Although high rates of LC were observed, strategies specific to the sacrum may require further optimization.
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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 | 0.002 |
| 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) | 0.001 | 0.000 |
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".