Stereotactic body radiotherapy for osseous low alpha–beta resistant metastases for pain relief—SOLAR-P
Bibliographic record
Abstract
BACKGROUND: Stereotactic Body Radiotherapy (SBRT) has shown effectiveness in treating bone metastases to alleviate pain. The benefit of SBRT may be further harnessed especially when radiating disease from primary malignancies with low alpha-beta ratios in order to maximize the magnitude and durability of pain relief. However, such an approach has not been studied in a prospective trial. We look to assess single-fraction SBRT for painful non-spinal bone metastases from radioresistant primaries. METHODS: Forty patients will be enrolled on an open label, phase II single arm trial to receive a single fraction of SBRT (15-20 Gray) to all sites of bone metastases requiring treatment for pain relief. Eligible patients will include those with primary malignancies consisting of prostate cancer, breast cancer, renal cell carcinoma, or melanoma. The primary endpoint is pain response at 3 months post-treatment using the Brief Pain Inventory. Secondary endpoints include pain response at 1 month and 6 months post-treatment, toxicity, patient-reported quality of life, re-irradiation or salvage surgery, and local control. DISCUSSION: This study will evaluate the efficacy of single-fraction SBRT on painful bone metastases from primary cancers with low alpha-beta ratios. These data will be valuable to promote future randomized trials and support clinical implementation. Trial registration Clinicaltrials.gov, NCT04177056. Date of registration: November 26, 2019. https://clinicaltrials.gov/ct2/show/NCT04177056.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".