Poster — Thur Eve — 15: Surface Dosimetric Performance of Superposition‐Convolution Algorithms in Tangential Photon Beams: A Monte Carlo Evaluation
Bibliographic record
Abstract
Surface dosimetry predicted by the analytical anisotropic algorithm (AAA) and collapsed cone convolution (CCC) algorithm was evaluated using oblique (5° and 45°) tangential photon beams (6 and 15 MV) with different field sizes (4 × 4, 7 × 7 and 10 × 10 cm2), produced by a Varian 21EX linac. Surface dose or phantom skin profiles, at a distance of 2 mm from the solid water phantom lateral surface to mimic skin doses, were calculated by the AAA, CCC and Monte Carlo simulation (EGSnrc‐based code) used as a benchmark for comparison. It was found that doses in the phantom skin profiles were underestimated with small fields for the 6 and 15 MV photon beams, when the gantry angle was set to 5° clockwise. The mean dose differences for the 6 MV (4 × 4 cm2) photon beams were −15.1% (SD = 3.6%) and −3.7% (SD = 1.5%) for the AAA and CCC, while those for the 15 MV (7 × 7 cm2) beams were −12% (SD = 3.5%) and −7.6% (SD = 2%) when compared to Monte Carlo simulations. For larger gantry angle of 45°, the AAA and CCC were found overestimating doses in the phantom skin profiles with different field sizes and beam energies. As surface dose with oblique tangential photon beam is important in radiation treatment sites such as breast, chest wall and sarcoma, the dosimetry data in this study are worthwhile to be considered, when carrying out quality assurance and commissioning for treatment planning systems.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".