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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".