Stereotactic body radiation therapy for non-spine bone metastases--a review of the literature.
Bibliographic record
Abstract
BACKGROUND: Stereotactic body radiation therapy (SBRT) has the ability to deliver significantly higher biologically equivalent doses (BED) compared to conventional radiation treatment. The main goal of SBRT is to improve local tumor control while reducing pain. The side effects however may be greater than those of conventional treatment. METHODS: A review of the literature was conducted and articles pertaining to studies of SBRT in non-spine bone metastases were included. Data on outcomes and toxicities were collected in addition to inclusion and exclusion criteria for each study. RESULTS: A total of 14 studies were included in this review. Very rarely were grade 3 and 4 toxicities reported. Endpoints for the studies varied significantly, which made conclusions of overall local control and progression free survival near impossible. In studies that reported local control rates, these rates were all greater than 85%. Progression free survival varied significantly between studies. CONCLUSIONS: Due to the lack of consistency in endpoint definitions, it is difficult to compare outcomes across trials. There is a need for consensus in endpoint definitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".