Improving the Integration between Palliative Radiotherapy and Supportive Care: A Narrative Review
Bibliographic record
Abstract
Palliative radiotherapy (PRT) is known to be effective in relieving cancer related symptoms. However, many studies and clinical practice show several barriers hindering its use and worsening the quality of patient support during PRT. Various solutions were proposed to overcome these barriers: training on PRT for supportive and palliative care specialists and training on palliative care for radiation oncologists, and introduction of pathways and organizational models specifically dedicated to PRT. Evidence on innovative organizational models and mutual training experiences is few and sparse. Therefore, the aim of this literature review is to present a quick summary of the information available on improving the PRT quality through training, new pathways, and innovative organizational models. The majority of studies on the integration of PRT with other palliative and supportive therapies present low levels of evidence being mostly retrospective analyses. However, it should be emphasized that all reports uniformly showed advantages coming from the integration of PRT with supportive therapies. To actively participate in the integration of PRT and palliative care, providing comprehensive support to the needs of patients with advanced cancer, radiation oncologists should not only plan PRT but also: (i) assess and manage symptoms and stress, (ii) rapidly refer patients to specialists in management of more complex symptoms, and (iii) participate in multidisciplinary palliative care teams. To this end, improved education in palliative care both in residency schools and during professional life through continuous medical education is clearly needed. In particular, effective training is needed for radiotherapy residents to enable them to provide patients with comprehensive palliative care. Therefore, formal teaching of adequate duration, interactive teaching methods, attendance in palliative care services, and education in advanced palliative care should be planned in post-graduated schools of radiotherapy.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".