Mixing chalk and cheese: the challenge for integrating complementary therapies into radiation oncology
Bibliographic record
Abstract
Abstract Background The supportive care of cancer patients receiving radiotherapy is an important responsibility for the radiation oncologist. A knowledgeable and empathic practitioner can gain the patients' trust to ensure that they receive appropriate management using evidence‐based interventions that are relatively safe. Objective To summarise the evidence‐based complementary interventions that can be used in radiation therapy. Methods A narrative review. Results There is good scientific evidence for both the effectiveness and ineffectiveness of specific complementary therapies used by radiation oncology patients. Conclusion Many radiation oncology programmes do not include supervised programmes for complementary therapies, despite patients' requirement for credible knowledge, and despite multiple clinical trials that have shown the utility for some interventions. It is important that the radiation oncologist can discriminate between effectiveness and ineffectiveness using knowledge transfer from appropriate clinical trials. Radiation oncology programmes should follow the evidence and implement techniques and therapies that reduce patient symptoms and contribute to their rehabilitation. As evidence builds for the effectiveness of certain complementary therapies, we are left with the challenge of integrating them into standard practice.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".