Poster — Thur Eve — 16: Dependences of Mucosal Dose on Small Photon Beams: A Monte Carlo Study
Bibliographic record
Abstract
This study investigates the dependences of the mucosal dose in the oral or nasal cavity on the small photon fields with different beam energies, beam angles and mucosa thicknesses. Phase space files of 6 and 18 MV photon beams (field size = 1× 1 cm2), produced by a Varian 21EX linac, were generated using the EGSnrc‐based BEAMnrc code. Mucosa phantoms (mucosa thickness = 1, 2 and 3 mm) with and without a bone under the mucosa were irradiated by photon beams with gantry angles varying from 0 to 30 deg. Doses along the central beam axis in the mucosa were calculated. For the 6 MV photon beams, it was found that the dose at the mucosa‐bone interface increased by 44.9% − 41.7%, when the mucosa thickness increased from 1 to 3 mm for the beam angle ranging from 0 to 30 deg. These values were lower than those (58.8% − 53.6%) for the 18 MV photon beams. For both the 6 and 18 MV photon beams, depth doses in the mucosa were found to increase with an increase of the beam angle. For the dose ratio (mucosal dose with bone to dose at the same point without bone), the dose enhancement due to the bone backscatter increased with a decrease of mucosa thickness, and was more significant at both the air‐mucosa and mucosa‐bone interface. The dosimetric information in this study is useful when searching for an optimized treatment strategy to minimize the mucosal complications in the head‐and‐neck IMRT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".