Utility of 30‐day mortality as a quality metric for palliative radiation treatment: A population‐based analysis from Queensland, Australia
Bibliographic record
Abstract
INTRODUCTION: Palliative radiotherapy (PRT) is frequently used to treat symptoms of advanced cancer, however benefits are questionable when life expectancy is limited. The 30-day mortality rate after PRT is a potential quality indicator, and results from a recent meta-analysis suggest a benchmark of 16% as an upper limit. In this population-based study from Queensland, Australia, we examined 30-day mortality rates following PRT and factors associated with decreased life expectancy. METHODS: Retrospective population data from Queensland Oncology Repository was used. Study population data included 22,501 patients diagnosed with an invasive cancer who died from any cause between 2008 and 2017 and had received PRT. Thirty-day mortality rates were determined from the date of last PRT fraction to date of death. Cox proportional hazards models were used to identify factors independently associated with risk of death within 30 days of PRT. RESULTS: Overall 30-day mortality after PRT was 22.2% with decreasing trend in more recent years (P = 0.001). Male (HR = 1.20, 95% CI = 1.13-1.27); receiving 5 or less radiotherapy fractions (HR = 2.97, 95% CI = 2.74-3.22 and HR = 2.17, 95% CI = 2.03-2.32, respectively) and receiving PRT in a private compared to public facility (HR = 1.61, 95% CI = 1.51-1.71) was associated with decreased survival. CONCLUSION: The 30-day mortality rate in Queensland following PRT is higher than expected and there is scope to reduce unnecessarily protracted treatment schedules. We encourage other Australian and New Zealand centres to examine and report their own 30-day mortality rate following PRT and would support collaboration for 30-day mortality to become a national and international quality metric for radiation oncology centres.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".