The LNT model for cancer induction is not supported by radiobiological data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it