Mucosal dosimetry on unflattened photon beams: a Monte Carlo phantom study
Bibliographic record
Abstract
Abstract Objective: This study investigated the dependences of mucosal dose and photon energy distribution on the flattened and unflattened photon beams using Monte Carlo simulation. Methods: A heterogeneous mucosa phantom with a variation of mucosal thickness (0.5–3 mm) was irradiated by the 6 and 10 MV flattened and unflattened photon beams generated by a Varian TrueBeam linac. The photon energy distributions at the mucosa and depth doses at the bones and mucosa were calculated using the EGSnrc-based Monte Carlo code. Results: It is found that the 6 MV unflattened photon beam contained more particle fluence in the low-energy range (0–100 keV) than the 10 MV. However, mucosal doses for the unflattened photon beams were found lower than the flattened. This is different from the skin dose having dose enhancement on the unflattened beam compared to the flattened, though both mucosal and skin structure involve an air-soft tissue interface. The particle fluence of the 6 and 10 MV unflattened photon beams increased with an increase of the mucosal thickness. This agreed well with the relative depth doses that increased in the ranges of 3.4%–3.6% and 2.5%–3.0% at the upper mucosa, and 5.1%–5.7% and 2.9%–3.2% at the lower mucosa, with their thickness increasing from 0.5–3 mm for the 6 and 10 MV unflattened photon beams. Conclusion: It is concluded that the unflattened photon beam results in a lower mucosal dose than the flattened, and the 6 MV unflattened beam has a larger dependence of mucosal dose on its thickness when compared to the 10 MV.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".