Dose perturbation effects in prostate seed implant brachytherapy with I-125
Bibliographic record
Abstract
EGSnrc Monte Carlo simulation was used to investigate dose perturbation effects in prostate seed implant brachytherapy using 125I radioactive seeds used in implant brachytherapy. Dose perturbation effects resulting from the seed mutual attenuation in a prostate seed implant consisting of 27 seeds were investigated. The results showed that for 125I seeds implanted into the prostate at 1.00 cm, 0.75 cm and 0.50 cm apart (uniform spacing), the dose perturbation effects are up to 10%. The volume of the target occupied by the 10% dose difference between the full Monte Carlo simulation and the single seed superposition model decreases with increasing seed spacing. Despite the differences between the Monte Carlo simulation and the simple superposition, there was no significant change in the dose volume histogram for 1 cm and 0.75 cm seed spacing. However, there was a significant change in the dose volume histogram when the seed spacing was 0.5 cm. An analysis of the external volume index (EI), coverage index (CI) and homogeneity index (HI) also showed that there is no difference in these indexes for the 1.00 cm and 0.75 cm seed spacing between the simple superposition model and the full Monte Carlo simulation. Compared to the full Monte Carlo simulations, the simple superposition model overestimated EI, CI and HI by 7%, 5% and 4% respectively for the 0.50 cm seed spacing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".