Bibliographic record
Abstract
The aim of this chapter is to give an overview on pain management in patients treated in radiation oncology. This chapter addresses the diverse symptom complex of pain in patients suffering from advanced cancer, while these symptoms may or may not be clearly associated with radiotherapy. Pain is one of the most challenging and most important symptoms among cancer patients and poses a complex problem with many different components, e.g., emotional, physical, and social, and may cause considerable distress and suffering throughout the course of disease and the applied therapies. Radiotherapy may be utilized to relieve symptoms of pain; some patients have preexisting pain resulting from the advanced disease or from comorbidities. However, pain may also be an important side effect of different therapeutic approaches, also of radiotherapy. Therefore supportive strategies should be interdisciplinary and equally multimodal to address this complexity. This chapter focuses on proposing practical advice for optimizing pain management in radiation oncology based on actual guidelines and reviews (Leitlinienprogramm Onkologie (Deutsche Krebsgesellschaft, Deutsche Krebshilfe, AWMF): S3-Leitlinie Palliativmedizin für Patienten mit einer nicht heilbaren Krebserkrankung, Langversion 1.0, 2015, AWMF-Registernummer: 128/001OL, http://leitlinienprogramm-onkologie.de/Palliativmedizin.80.0.html , 2015; Deutsche Gesellschaft für Schmerzmedizin (DGS) PraxisLeitlinie Tumorschmerz V 2.0, https://dgschmerzmedizin.de/praxisleitlinien/Tumorschmerz.pdf , 2014; Sawhney et al., Guidelines on Management of Pain in Cancer and/or Palliative Care. Cancer Care Ontario, https://archive.cancercare.on.ca/common/pages/UserFile.aspx?fileId=384144 , 2017; Fallon et al., Ann Oncol 29(4):iv149–iv174, 2018).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.036 |
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".