Surgical resection of epidural disease improves local control following postoperative spine stereotactic body radiotherapy
Bibliographic record
Abstract
BACKGROUND: Spine stereotactic body radiotherapy (SBRT) is increasingly being applied to the postoperative spine metastases patient. Our aim was to identify clinical and dosimetric predictors of local control (LC) and survival. METHODS: Eighty patients treated between October 2008 and February 2012 with postoperative SBRT were identified from our prospective database and retrospectively reviewed. RESULTS: The median follow-up was 8.3 months. Thirty-five patients (44%) were treated with 18-26 Gy in 1 or 2 fractions, and 45 patients (56%) with 18-40 Gy in 3-5 fractions. Twenty-one local failures (26%) were observed, and the 1-year LC and overall survival (OS) rates were 84% and 64%, respectively. The most common site of failure was within the epidural space (15/21, 71%). Multivariate proportional hazards analysis identified systemic therapy post-SBRT as the only significant predictor of OS (P = .02) and treatment with 18-26 Gy/1 or 2 fractions (P = .02) and a postoperative epidural disease grade of 0 or 1 (0, no epidural disease; 1, epidural disease that compresses dura only, P = .003) as significant predictors of LC. Subset analysis for only those patients (n = 48/80) with high-grade preoperative epidural disease (cord deformed) indicated significantly greater LC rates when surgically downgraded to 0/1 vs 2 (P = .0009). CONCLUSIONS: Postoperative SBRT with high total doses ranging from 18 to 26 Gy delivered in 1-2 fractions predicted superior LC, as did postoperative epidural grade.
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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 | 0.002 |
| 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".