Poster — Wed Eve—25: Comparison of Dose Calculation Algorithms with Monte Carlo Simulation for Surface Dosimetry
Bibliographic record
Abstract
The aim of this study is to compare the surface dosimetry calculated by the analytical anisotropic algorithm (AAA) and collapsed cone convolution (CCC) algorithm with Monte Carlo (MC) simulation. The MC simulations are verified by measurements. In this study, tangential photon beams (6 and 15 MV; 4 × 4 and ), produced by a Varian 21 EX linear accelerator, with central beam axis (CAX) parallel to the water phantom surface were used. In addition, the photon beam was tilted clockwise 3 and 5 degrees around the isocenter located at a distance of 2 mm from the phantom surface to represent the skin thickness. Relative dose profiles (2 mm from the phantom surface) corresponding to the above experimental configuration were calculated using the AAA, CCC and MC methods based on the Eclipse, Pinnacle3 treatment planning system and the EGSnrc code. It is found that both the AAA and CCC methods agreed with uncertainty < ±3% compared to the MC, in calculating the relative surface dose profiles, when the CAXs of the photon beams were overlapped along the profiles. However, when the photon beams (6 and 15 MV; 4 × 4 and ) were tilted clockwise, the AAA and CCC methods underestimated the dose in the relative surface profile as compared to the MC. The deviation of the calculated relative surface dose profile among the AAA, CCC and MC methods depends on the energy, angle and field size of the photon beam.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".