Ethics-Guided Radiation Therapy (EGRT): A necessity in radiation oncology practice
Bibliographic record
Abstract
Medical ethics principles have been the basis of medical practice since early human civilization. The well-accepted principles are autonomy, beneficence, non-maleficence, and justice. With the advancement of academia, industry, medicine, and technology, there is a need to empower ethics-guided radiation therapy (EGRT). A PubMed search was done on Oct. 22, 2023, using the words: (“Radiotherapy”[Mesh]) AND “Ethics, Clinical”[Mesh]) and the results were a total of 58. Among these, 17 titles seem to be in relation, but only a handful were of intimate relation to ethics and radiotherapy. An additional handful of non-PubMed references were found. EGRT, in my opinion, is a new acronym for an old concept that needs further elaboration and experts’ consensus in the modern radiation oncology literature. In parallel with the technological advances in radiotherapy, like intensity-modulated radiation therapy “IMRT” and image-guided radiation therapy “IGRT,” we are aiming to create an initiative to establish EGRT to be like a model that every radiation oncologist can follow in the daily radiotherapy practice. The coming work will be composed of an extensive literature review, international survey, and expert consensus, and it is intended to be a base for further efforts in this aspect.
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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".