Postoperative Stereotactic Body Radiotherapy for Spinal Metastases and the Impact of Epidural Disease Grade
Bibliographic record
Abstract
BACKGROUND: Postoperative stereotactic body radiotherapy (pSBRT) is an emerging indication for spinal metastases (SM). OBJECTIVE: To report our experience with pSBRT for SM. METHODS: A retrospective chart review was performed for prospectively collected data of patients treated between September 2008 to December 2015 with pSBRT and followed with serial spinal MRIs every 2 to 3 mo until death or last follow-up. Univariate and multivariable analyses were performed to identify predictive factors. RESULTS: A total of 83 spinal segments in 47 patients treated with a median dose of 24 Gy in 2 fractions were included, with mostly lung and breast primaries. A total of 59.3% had preoperative high-grade epidural disease (ED) and 39.7% were unstable. The 12-mo cumulative incidence of local failure was 17% for all segments, and 33.3%, 21.8%, and 0% in segments with postoperative high-grade, low-grade, and no ED, respectively. Downgrading preoperative ED was predictive of better local control (P = .03). The grade of postoperative ED was also predictive for local control (P < .0001), as was a longer interval between prior radiotherapy and pSBRT in those previously irradiated (P = .004). The 12-mo overall survival rate was 55%. One case of radiculopathy, 3 vertebral compression fractures, and no cases of myelopathy, hardware failure, or skin breakdown were observed. CONCLUSION: pSBRT is an effective and safe treatment. The association between downgrading preoperative ED and better local control following pSBRT is confirmed and supports the concept of separation surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".