Position statement on ethics, equipoise and research on charged particle radiation therapy
Bibliographic record
Abstract
The use of charged-particle radiation therapy (CPRT) is an increasingly important development in the treatment of cancer. One of the most pressing controversies about the use of this technology is whether randomised controlled trials are required before this form of treatment can be considered to be the treatment of choice for a wide range of indications. Equipoise is the key ethical concept in determining which research studies are justified. However, there is a good deal of disagreement about how this concept is best understood and applied in the specific case of CPRT. This report is a position statement on these controversies that arises out of a workshop held at Wolfson College, Oxford in August 2011. The workshop brought together international leaders in the relevant fields (radiation oncology, medical physics, radiobiology, research ethics and methodology), including proponents on both sides of the debate, in order to make significant progress on the ethical issues associated with CPRT research. This position statement provides an ethical platform for future research and should enable further work to be done in developing international coordinated programmes of research.
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 | 0.146 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.084 | 0.069 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".