Palliative radiotherapy at the end of life: A critical review
Bibliographic record
Abstract
When delivered with palliative intent, radiotherapy can help to alleviate a multitude of symptoms related to advanced cancer. In general, time to symptom relief is measured in weeks to months after the completion of radiotherapy. Over the past several years, an increasing number of studies have explored rates of radiotherapy use in the final months of life and have found variable rates of radiotherapy use. The optimal rate is unclear, but would incorporate anticipated efficacy in patients whose survival allows it and minimize overuse among patients with expected short survival. Clinician prediction has been shown to overestimate the length of survival in repeated studies. Prognostic indices can provide assistance with estimations of survival length and may help to guide treatment decisions regarding palliative radiotherapy in patients with potentially short survival times. This review explores the recent studies of radiotherapy near the end of life, examines general prognostic models for patients with advanced cancer, describes specific clinical circumstances when radiotherapy may and may not be beneficial, and addresses open questions for future research to help clarify when palliative radiotherapy may be effective near the end of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".