The Future of Radiobiology
Bibliographic record
Abstract
Innovation and progress in radiation oncology depend on discovery and insights realized through research in radiation biology. Radiobiology research has led to fundamental scientific insights, from the discovery of stem/progenitor cells to the definition of signal transduction pathways activated by ionizing radiation that are now recognized as integral to the DNA damage response (DDR). Radiobiological discoveries are guiding clinical trials that test radiation therapy combined with inhibitors of the DDR kinases DNA-dependent protein kinase (DNA-PK), ataxia telangiectasia mutated (ATM), ataxia telangiectasia related (ATR), and immune or cell cycle checkpoint inhibitors. To maintain scientific and clinical relevance, the field of radiation biology must overcome challenges in research workforce, training, and funding. The National Cancer Institute convened a workshop to discuss the role of radiobiology research and radiation biologists in the future scientific enterprise. Here, we review the discussions of current radiation oncology research approaches and areas of scientific focus considered important for rapid progress in radiation sciences and the continued contribution of radiobiology to radiation oncology and the broader biomedical research community.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".