Risk of Radiation-induced Breast Cancer from Mammographic Screening
Bibliographic record
Abstract
PURPOSE: To assess a schema for estimating the risk of radiation-induced breast cancer following exposure of the breast to ionizing radiation as would occur with mammography and to provide data that can be used to estimate the potential number of breast cancers, cancer deaths, and woman-years of life lost attributable to radiation exposure delivered according to a variety of screening scenarios. MATERIALS AND METHODS: An excess absolute risk model was used to predict the number of radiation-induced breast cancers attributable to the radiation dose received for a single typical digital mammography examination. The algorithm was then extended to consider the consequences of various scenarios for routine screening beginning and ending at different ages, with examinations taking place at 1- or 2-year intervals. A life-table correction was applied to consider reductions of the cohort size over time owing to nonradiation-related causes of death. Finally, the numbers of breast cancer deaths and woman-years of life lost that might be attributable to the radiation exposure were calculated. Cancer incidence and cancer deaths were estimated for individual attained ages following the onset of screening, and lifetime risks were also calculated. RESULTS: For a cohort of 100 000 women each receiving a dose of 3.7 mGy to both breasts and who were screened annually from age 40 to 55 years and biennially thereafter to age 74 years, it is predicted that there will be 86 cancers induced and 11 deaths due to radiation-induced breast cancer. CONCLUSION: For the mammographic screening regimens considered that begin at age 40 years, this risk is small compared with the expected mortality reduction achievable through screening. The risk of radiation-induced breast cancer should not be a deterrent from mammographic screening of women over the age of 40 years.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".