Stereotactic Body Radiation Therapy for Spine Metastases—Findings from an Australian Population-Based Study
Bibliographic record
Abstract
Background: To evaluate the use of stereotactic body radiation therapy (SBRT) for spine metastases and the associated factors in Australia. Methods: The Victorian Radiotherapy Minimum Dataset, which captures all episodes of radiotherapy delivered in the state of Victoria, was accessed to evaluate the patterns and trends of SBRT for spine metastases. The primary outcome was SBRT use and associated factors. Results: There were 6244 patients who received 8861 courses of radiotherapy for spine metastases between 2012 and 2017. Of these, 277 (3%) courses were SBRT, which increased from 0.4% in 2012 to 5% in 2017 (P-trend < 0.001). There was a higher proportion of SBRT use in patients with prostate cancer (6%) and melanoma (4%) compared to other cancers (2–3%) (p < 0.001). Patients from the highest socioeconomic quintiles (5%) were more likely to be treated with SBRT compared to patients from the lowest socioeconomic quintiles (3%) (p < 0.001). There was a higher proportion of SBRT use in private radiotherapy centres (6%) compared to public radiotherapy centres (1%) (p < 0.001). No spine SBRT was delivered in regional centres. In multivariate analyses, the year of treatment, age, primary cancers and radiotherapy centres were independently associated with SBRT use. Conclusion: This is the first Australian population-based study quantifying the increasing use of spine SBRT; however, the overall use of spine SBRT remains low. We anticipate an ongoing increase in spine SBRT, as spine SBRT gradually becomes the standard-of-care treatment for painful spine metastases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".