Appropriateness of the 30-day expected mortality metric in palliative radiation treatment: a narrative review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The 30-day expected mortality rate is frequently used as a metric to determine which patients benefit from palliative radiation treatment (RT). We conducted a narrative review to examine whether its use as a metric might be appropriate for patient selection. METHODS: A literature review was conducted to identify relevant studies that highlight the benefits of palliative RT in timely symptom management among patients with a poor performance status, the accuracy of predicting survival near the end of life and ways to speed up the process of RT administration through rapid response clinics. KEY CONTENT AND FINDINGS: Several trials have demonstrated substantial response rates for pain and/or bleeding by four weeks and sometimes within the first two weeks after RT. Models of patient survival have limited accuracy, particularly for predicting whether patients will die within the next 30 days. Dedicated Rapid Access Palliative RT (RAPRT) clinics, in which patients are assessed, simulated and treated on the same day, reduce the number of patient visits to the radiation oncology department and hence the burden on the patient as well as costs. CONCLUSIONS: Single-fraction palliative RT should be offered to eligible patients if they are able to attend treatment and could potentially benefit from symptom palliation, irrespective of predicted life expectancy. We discourage the routine use of the 30-day mortality as the only metric to decide whether to offer RT. More common implementation of RAPRT clinics could result in a significant benefit for patients of all life expectancies, but particularly those having short ones.
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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 | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".