Initial evaluation of Advanced Collapsed cone Engine dose calculations in water medium for I‐125 seeds and <scp>COMS</scp> eye plaques
Bibliographic record
Abstract
Purpose To investigate the dose calculation accuracy in water medium of the Advanced Collapsed cone Engine (ACE) for three sizes of COMS eye plaques loaded with low‐energy I‐125 seeds. Methods A model of the Oncura 6711 I‐125 seed was created for use with ACE in Oncentra® Brachy (OcB) using primary‐scatter separated (PSS) point dose kernel and Task Group (TG) 43 datasets. COMS eye plaque models of diameters 12, 16, and 20 mm were introduced into the OcB applicator library based on 3D CAD drawings of the plaques and Silastic inserts. To perform TG‐186 level 1 commissioning, treatment plans were created in OcB for a single source in water and for each COMS plaque in water for two scenarios: with only one centrally loaded seed, or with all seed positions loaded. ACE dose calculations were performed in high accuracy mode with a 0.5 × 0.5 × 0.5 mm3 calculation grid. The resulting dose data were evaluated against Monte Carlo (MC) calculated doses obtained with MCNP6, using both local and global percent differences. Results ACE doses around the source for the single seed in water agreed with MC doses on average within < 5% inside a 6 × 6 × 6 cm3 region, and within < 1.5% inside a 2 × 2 × 2 cm3 region. The PSS data were generated at a higher resolution within 2 cm from the source, resulting in this improved agreement closer to the source due to fewer approximations in the ACE dose calculation. Average differences in both investigated plaque loading patterns in front of the plaques and on the plaque central axes were ≤ 2.5%, though larger differences (up to 12%) were found near the plaque lip. Conclusions Overall, good agreement was found between ACE and MC dose calculations for a single I‐125 seed and in front of the COMS plaques in water. More complex scenarios need to be investigated to determine how well ACE handles heterogeneous patient materials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".