Sci‐Sat AM(2): Brachy‐05: Dosimetry effects of the TG‐43 approximations for two iodine seeds in LDR brachytherapy
Bibliographic record
Abstract
PURPOSES: This work consists of studying the interseed and tissue composition effects for two model iodine seeds: the IBt Interseed-125 and the 6711 model seed. MATERIALS & METHODS: Three seeds were modeled with the MCNP MC code in a water sphere to evaluate the interseed effect. The dose calculated at different distances from the centre was compared to the dose summed when the seeds were simulated separately. The tissue composition effect was studied calculating the radial dose function for different tissues. Before carrying out post-implant studies, the absolute dose calculated by MC was compared to experiment results: with LiF TLDs in an acrylic breast phantom and with an EBT Gafchromic film placed in a water tank. Afterwards, the TG-43 approximation effects were studied for a prostate and breast post-implant. RESULTS AND DISCUSSION: The interseed effect study shows that this effect is more important for model 6711 (15%) than for IBt (10%) due to the silver rod in 6711. For both seed models the variations of the radial dose function as a function of the tissue composition are quasi similar. The absolute dose comparisons between MC calculations and experiments give good agreement (inferior to 3% in general). For the prostate and breast post-implant studies, a 10% difference between MC calculations and the TG-43 is found for both models of seeds. CONCLUSION: This study shows that the differences in dose distributions between TG43 and MC are quite similar for the two models of seeds and are about 10% for the studied post-implant treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".