238 Stereotactic Body Radiotherapy for Oligometastatic Cancer: A Rapid Review of the Clinical Effectiveness and Cost-Effectiveness
Bibliographic record
Abstract
CARO-ASM 2019guidance and tumour localization.Review of patients treated is important to ensure that high degree of local control is being achieved, with reasonable toxicity.Objectives: To perform a quality assurance review of patients treated to date, and to report the local control and survival in the patients treated. Materials and Methods:Retrospective chart review, was performed, after local quality board / REB approval to review patient records for the purpose of quality assurance.The Varian ARIA database was queried to find patients treated with SBRT.Results: Thirty-nine sites in 34 patients were treated between April 2015 and January 2019, using linac-based SBRT with the Varian Trubeam, and the Brainlab Exactrac imaging system.The average age of patients treated was 67.4 (range: 42 to 86).Thirtyone sites received SBRT to spine, the remainder received SBRT to non-spine bone lesions.The origin of the metastases and number of sites were as follows: prostate (17), lung (nine), kidney (seven), thyroid (four), sarcoma (one), and melanoma (one).From 39 sites, 34 were controlled at last assessment (87.2%).The modal dose utilized was 24 Gy in 2 fractions.Local control at three years was significantly better for prostate metastases versus non-prostate metastases (100% versus 50%, log-rank p-value = 0.043).One patient treated with spinal SBRT for Kidney cancer has suffered a vertebral fracture, using a dose of 24 Gy in 2fr.Overall survival for all patients is 82.7% at three years.Overall survival is significantly better in the patients with prostate cancer versus other primary sites at three years (100 versus 65%, log-rank p-value = 0.0339) Conclusions: SBRT for bone metastases appear to result in high rates of local control, especially for patients with prostate cancer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".