Biological Factors Influencing the RBE of Neutrons: Implications for Their Past, Present and Future Use in Radiotherapy
Bibliographic record
Abstract
The recent resurgence of interest in fast-neutron therapy, particularly for the treatment of prostate cancer, warrants a review of the original radiobiological basis for this modality and the evolution of these concepts that resulted from subsequent experimentation with the fast-neutron beams used for randomized clinical trials. It is clear from current radiobiological knowledge that some of the postulates that formed the mechanistic basis for past clinical trials were incorrect. Such discrepancies, along with the inherent physical disadvantages of neutron beams in terms of collimation and intensity modulation, may partially account for the lack of therapeutic benefit observed in many randomized clinical trials. Moreover, it is equally apparent that indiscriminate prescription of fast-neutron therapy is likely to lead to an adverse clinical outcome in a proportion of patients. Hence any renewed efforts to establish a niche for this modality in clinical radiation oncology will necessitate the development of a triage system that can discriminate those patients who might benefit from fast-neutron therapy from those who might be harmed by it. In the future, fast-neutron therapy might be prescribed based upon the relative status of appropriate molecular parameters that have a differential impact upon radiosensitivity to photons compared to fast neutrons.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".