A critical appraisal of the four systematic reviews and meta-analysis on stereotactic body radiation therapy versus external beam radiotherapy for painful bone metastases and where we go from here
Bibliographic record
Abstract
Radiotherapy is an important treatment modality for pain control in patients with bone metastases. Stereotactic body radiation therapy (SBRT), which allows delivering a much higher dose per fraction while sparing critical structures compared to conventional external beam radiotherapy (cEBRT), has become more widely used, especially in the oligometastatic setting. Randomized controlled trials (RCTs) comparing the pain response rate of SBRT and cEBRT for bone metastases have shown conflicting results, as have four recent systematic reviews with meta-analyses of these trials. Possible reasons for the different outcomes between these reviews include differences in methodology, which trials were included, and the endpoints examined and how they were defined. We suggest ways to improve analysis of these RCTs, particularly performing an individual patient-level meta-analysis since the trials included heterogeneous populations. The results of such studies will help guide future investigations needed to validate patient selection criteria, optimize SBRT dose schedules, include additional endpoints (such as the time to onset of pain response, durability of pain response, quality of life (QOL), and side effects of SBRT), and better assess the cost-effectiveness and trade-offs of SBRT compared to cEBRT. An international Delphi consensus to guide selection of optimal candidates for SBRT is warranted before more prospective data is available.
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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 | 0.070 | 0.197 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".