Non-targeted effects and radiation-induced carcinogenesis: a review
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
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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