Skin dose in chest wall radiotherapy with bolus: a Monte Carlo study
Bibliographic record
Abstract
Monte Carlo simulations are used to investigate skin dose resulting from chest wall radiotherapy with bolus. A simple model of a female thorax is developed, which includes a 2 mm-thick skin layer. Two representative 6 MV source models are considered: a tangents source model consisting of a parallel opposed pair of medial and lateral fields and subfields, and an arc source model. Tissue equivalent (TE) boluses (thicknesses of 3, 5 and 10 mm) and brass mesh bolus are considered. Skin dose distributions depend on incident photon obliquity: for tangents, radiation is incident more obliquely, resulting in longer path lengths through the bolus and higher skin dose compared to the arc source model in most cases. However, for thicker TE boluses, attenuation of oblique photons becomes apparent. Brass bolus and 3 mm TE bolus result in similar mean skin dose. This relatively simple computational model allows for consideration of different bolus thicknesses, materials and usage schedules based on desired skin dose and choice of either tangents or an arc beam technique. For example, using a 5 mm TE bolus every second treatment would result in mean skin doses of 89% and 85% for tangents and the arc source model, respectively. The hot spot metric D[Formula: see text] would be 103% and 99%, respectively.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".