Role of radiation therapy and radiopharmaceuticals in bone metastases
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bone metastases remain the most common cause of cancer-related pain. Palliative radiotherapy and radiopharmaceuticals are effective for symptom control, but continue to be underutilized. We review recent literature for the treatment of bone metastases and spinal cord compression, and address new developments in the prevention of adverse effects secondary to radiotherapy. RECENT FINDINGS: Evidence continues to mount in support of the efficacy of short-course radiation schedules. Emerging data support the use of single fractions. While radiotherapy is relatively nontoxic, pain flare can be a distressing side effect. Recent reports suggest that traditional and more innovative radiopharmaceuticals are well tolerated, with effects comparable with external beam radiation and the added advantage of addressing multiple, widespread painful sites simultaneously. SUMMARY: There is an urgent need for more randomized controlled trials in the radiotherapy of complicated bone metastases, such as the settings of spinal cord compression and neuropathic pain. Additional study of radiopharmaceuticals as adjuvants to external beam radiotherapy would also serve to further elucidate the optimal treatment for these patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.002 |
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".