Monte Carlo calculation of <sup>119</sup> Sb microscale absorbed dose using cascaded and averaged Auger electron spectra
Bibliographic record
Abstract
Radionuclides decaying by electron capture or internal transition produce a large number of Auger electrons in a cascade that follows their radioactive decay. A shortlist of the most potent Auger electron-emitters has appeared in the literature including103mRh,103Pd,111In,119Sb,123I,125I,165Er, and197Hg. Among them,119Sbhas been identified as the most potent for targeting micrometastases, yielding several tens of Auger electrons per decay with energies from a few eV up to 30 keV. In this paper, we recalculate Auger, Coster-Kronig, and super Coster-Kronig yields and transition probabilities as subshell-normalized relative transition probabilities and develop a new method to create radionuclide sources in TOPAS Monte Carlo, the code for which has been made publicly available. We then apply our method to encode the Auger electron spectra of119Sbfrom MIRD RADTABS and EADL into TOPAS and calculate the absorbed dose to water volumes of radius10nmup to10μm, finding that the averaged MIRD Auger electron spectrum underestimates the absorbed dose by a factor of 20 to 50 on this scale. We show that this result is not isolated to119Sband conclude that either the cascaded MIRD or EADL spectrum should be used for accurate microscale dosimetry. We compare with results obtained using the built-in Geant4 Atomic Relaxation for119Sbin TOPAS and find an unexpected continuum of low-energy electrons but no excess absorbed dose relative to either MIRD or EADL. We show that119Sbdoes not produce more absorbed dose in microscale volumes than103mRh,103Pd,111In,123I,125I,165Er, or197Hg, warranting future microdosimetry calculations of RBE and DNA damage to understand whether119Sbis the most potent Auger electron-emitter, as claimed in the literature.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".