SU‐GG‐T‐438: Dose to Medium or Dose to a Water Cavity Embedded in Medium? A Monte Carlo Study
Bibliographic record
Abstract
Purpose: The target for radiotherapy is to sterilize cells by imposing damage to their DNA content. It is therefore of interest to study the relations between the dose calculated to a tissue medium, Dm,m, with an representative average atomic composition versus the dose Dn,m specifically absorbed by the cell nuclei, and the dose Dw,m to a cell nuclei surrogate represented as a water cavity embedded into tissue. The simulations were performed for six types of tissues and three different brachytherapy sources. Methods and Materials: Absorbed dose calculations were performed by GEANT4 MC code version 9.2 using the Penelope physics package. Three different sources, 192Ir to represent high energy conditions, 169Yb for intermediate energies and a low energy brachytherapy source 125I, were simulated. The photon spectra used in this study were taken from http://www.physics.carleton.ca/clrp/seed_database. The 192Ir spectra was from Nucletron, microSelectron‐HDR v2 and for 125I from Nucletron, SelectSeed, 130.002. Particle spectra for 192Ir and energy spectra for 192I were also scored to calculate the dose with different cavity theories and compare it with MC calculated doses. Results: The MC calculated Dm,m, Dw,m and Dn,m ratio shows the largest value for 125I brachytherapy source and the cortical bone material, adipose tissue and prostate tissues(ICRU). The difference between Dw,m and Dn,m is also largest for the cortical bone and 125I source. For the 192Ir source, the ratio was 1 for the soft tissues while for the cortical bone it was higher. Conclusion: Monte Carlo calculations made it possible to report the dose as absorbed dose to medium. Accurate reporting of treatment planning requires a stringent standard for dose definition. MC calculations can be used to develop conversion factors to convert dose according to different definitions for different brachytherapy sources.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".