Microdosimetric Analysis of Various Mammography Spectra: Lineal Energy Distributions and Ionization Cluster Analysis
Bibliographic record
Abstract
In view of recent recommendations on the frequency and the starting age of mammography screening in healthy women, it is desirable to quantify the enhanced relative biological effectiveness (RBE) of mammography X rays compared to hard X rays. While there is little doubt that the former are more potent in inducing biological damage than the latter, the magnitude of the effect is still hotly debated in the literature. We used Monte Carlo simulations and track structure analysis in micrometer and nanometer volumes to investigate differences in distributions of lineal energy and ionization clusters for a range of mammography X-ray qualities. Dose-averaged lineal energies, (yD), in breast tissue for various mammography qualities were found to result in quality factors about 40% higher than unity. Among the various mammography qualities studied, the popular molybdenum/molybdenum target/filter combination was found to have the highest (yD) in 1-microm spheres (about 5.0 keV/microm near the entrance surface of breast tissue). In 10-nm radius spheres, the mean ionization cluster order was found to be about 35% higher in mammography X rays compared to 300 keV electrons (roughly representing 60Co or 192Ir photon radiation). In even smaller spheres (2 nm radius), no significant differences were observed for the mean ionization cluster order between mammography X rays and 300 keV electrons. We conclude that the potential of mammography X rays to induce biological damage is probably not much higher than a factor of two compared to hard X rays.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".