Bibliographic record
Abstract
With the possible exception of radiation-induced leukemia, more is known about radiation-induced breast cancer than any other malignancy. Fourteen cohort studies have provided quantitative information on the level of risk following a wide range of doses in different populations around the world. Comprehensive studies have been conducted in Canada, Germany, Japan, Sweden and other Nordic countries, the United Kingdom, and the USA [Table I in text]. Key features are the linearity in the dose response (i.e., a straight line adequately fits the observed data), and the effect modification of age at exposure (i.e., risk is inversely related to exposure age and exposures past the menopausal ages appear to carry a very low risk); and the minimal effect of fractionating dose on subsequent risk. A recent combined analysis of almost 78,000 women and 1,500 breast cancer cases from eight cohorts confirmed the downturn in risk at the highest dose levels (related in part to the killing of cells rather than transformation) and that fractionation of dose has little influence on risk, at least on an absolute scale. It is not known whether persons predisposed to cancer are at enhanced risk of radiation-induced breast cancer from low-dose exposures, although this seems unlikely. New data on the effects of high doses following childhood exposures will be forthcoming from long-term studies of the survivors of childhood cancer.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".