Palliative radiotherapy for bone metastases: Population-based utilization near end of life in a Canadian province.
Bibliographic record
Abstract
9523 Background: Palliative radiotherapy (RT) plays an important role in the care of patients dying with bone metastases (BM) and is guided by life expectancy . We sought to determine the patterns of RT fractionation in patients with BM towards their end of life in a population-based, publicly funded health care system. Methods: Consecutive patients with BM treated with RT between 2007 through 2011 were identified in a provincial Canadian cancer registry database. Associations between choice of RT fractionation, patient and provider characteristics were analyzed. Results: A total of 16,898 courses of palliative RT were delivered to 8,601 patients from 2007 through 2011. Of these RT courses 1,734 (10%) and 709 (4%) were prescribed to patients in the last 2-4 weeks and <2 weeks of their life, respectively. Multiple fraction RT was prescribed 52%, 46%, and 36% for patients who died >4, 2-4, and <2 weeks of receiving palliative RT, respectively (p<0.001). Fifteen percent of courses were planned for ≥5 RT fractions in patients who died within 2 weeks of receiving palliative RT. The proportion of patients who died within 4 weeks of RT varied by primary tumour site; lung 25%, gastrointestinal 23%, genitourinary 11%, lymphoma 9%, breast 6%, and other 19.4% (p < 0.001). The proportion of patients who died within 4 weeks of RT varied by treatment site; spine 17%, extremity 14%, pelvis 12%, other 13% (p < 0.001). After controlling for gender, age, tumour type, and treatment site, patients were less likely to receive multiple fraction RT in the last 4 weeks of life (OR 0.54; 95% CI 0.49 – 0.59; p <0.0001). Conclusions: This population-based analysis found that 14% of patients with bone metastases received radiotherapy during the last 4 weeks of their life. There was significant variability by primary tumour type, with approximately ¼ of lung and gastrointestinal patients receiving RT with the last 4 weeks of life, suggesting physicians treating these sites should consider benefits of RT versus the short prognosis in these tumour sites. Appropriately, patients who received RT with the last 2 or 2-4 weeks of life were less likely to receive a multiple fraction RT course compared to patients alive >4 weeks since RT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".