Non-targeted effects and radiation-induced carcinogenesis: a review
Bibliographic record
Abstract
Exposure to ionising radiation is clearly associated with an increased risk of developing some types of cancer. However, the contribution of non-targeted effects to cancer development after exposure to ionising radiation is far less clear. The currently used cancer risk model by the international radiation protection community states that any increase in radiation exposure proportionately increases the risk of developing cancer. However, this stochastic cancer risk model does not take into account any contribution from non-targeted effects. Nor does it consider the possibility of a bystander mechanism in the induction of genomic instability. This paper reviews the available evidence to date for a possible role for non-targeted effects to contribute to cancer development after exposure to ionising radiation. An evolution in the understanding of the mechanisms driving non-targeted effects after exposure to ionising radiation is critical to determine the true contribution of non-targeted effects on the risk of developing cancer. Such an evolution will likely only be achievable through coordinated multidisciplinary teams combining several fields of study including: genomics, proteomics, cell biology, molecular epidemiology, and traditional epidemiology.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".