Palliative Radiotherapy Trials for Bone Metastases: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The objective is to update previous meta-analyses with a systematic review of randomized palliative radiotherapy (RT) trials comparing single fractions (SFs) versus multiple fractions (MFs). METHODS: The analysis includes all published reports from randomized trials comparing SF or MF schedules for the treatment of painful bone metastases with localized RT. A systematic review was performed using the random-effects model with Review Manager version 4.1 (Cochrane Collaboration, Oxford, UK). The odds ratio and 95% CI were calculated for each trial and presented in a forest plot. RESULTS: A total of 16 randomized trials from 1986 onward were identified. For intention-to-treat patients, the overall response (OR) rates for pain were similar for SF at 1,468 (58%) of 2,513 patients and MF RT at 1,466 (59%) of 2,487 patients. The complete response (CR) rates for pain were 23% (545 of 2,375 patients) for SF and 24% (558 of 2,351 patients) for MF RT. No significant differences were found in response rates. Trends showing an increased risk for SF RT arm patients in terms of pathological fractures and spinal cord compressions were observed, but neither were statistically significant (P = .75 and P = .13, respectively). The likelihood of re-treatment was 2.5-fold higher (95% CI, 1.76 to 3.56) in SF RT arm patients (P < .00001). Repeated analysis of these end points, excluding dropout patients, did not alter the conclusions. Generally, no significant differences with respect to acute toxicities were observed between the arms. CONCLUSION: No significant differences in the arms were observed for overall and CR rates in both intention-to-treat and assessable patients. However, a significantly higher re-treatment rate with SFs was evident.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".