Role of Radionuclide Therapy as Adjuvant to Palliative External Beam Radiotherapy for Painful Multiple Skeletal Metastasis
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate the palliative efficacy of localized external radiotherapy (RT) combined with systemic radionuclide (RN) therapy in patients who had multiple painful osseous metastases of different primary origins. METHODS: Thirty-three patients initially local external radiotherapy was delivered to the most symptomatic region in all patients. Then they received either Re 186 HEDP or Sm 153 EDTMP. The performance status was assessed according to ECOG scale. Before treatment, at the end of the radiotherapy and after the four weeks of systemic radionuclide therapy, analgesic intake and pain status were recorded by the RTOG scoring system, and EORTC QLQ C30 (Version 3.0 Turkish) questionnaire was performed to evaluate the quality of life. RESULTS: Improved performances of 33.3% for post radiation therapy and 50% for post radionuclide therapy in the ECOG scale were observed. Statistically significant correlations were found between the primary origins and decreased pain and analgesic intake (p < 0.05), but no differences were observed on the self assessment quality of life questionnaire. CONCLUSIONS: Both Re 186 HEDP, Sm 153 EDTMP are effective and safe in bone pain palliation as an adjuvant to local field radiation therapy of breast and prostate cancer patients, who also continued to receive chemotherapy and/or hormontherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".