The LNT model for cancer induction is not supported by radiobiological data
Bibliographic record
Abstract
The hallmarks of cancer have been the focus of much research and have influenced the development of risk models for radiation-induced cancer. However, natural defenses against cancer, which constitute the hallmarks of cancer prevention, have largely been neglected in developing cancer risk models. These natural defenses are enhanced by low doses and dose rates of ionizing radiation, which has aided in the continuation of human life over many generations. Our natural defenses operate at the molecular, cellular, tissue, and whole-body levels and include epigenetically regulated (epiregulated) DNA damage repair and antioxidant production, selective p53-independent apoptosis of aberrant cells (e.g. neoplastically transformed and tumor cells), suppression of cancer-promoting inflammation, and anticancer immunity (both innate and adaptive components). This publication reviews the scientific bases for the indicated cancer-preventing natural defenses and evaluates their implication for assessing cancer risk after exposure to low radiation doses and dose rates. Based on the extensive radiobiological evidence reviewed, it is concluded that the linear-no-threshold (LNT) model (which ignores natural defenses against cancer), as it relates to cancer risk from ionizing radiation, is highly implausible. Plausible models include dose-threshold and hormetic models. More research is needed to establish when a given model (threshold, hormetic, or other) applies to a given low-dose-radiation exposure scenario.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".