Poster — Wed Eve—24: Dosimetry of Lead Shield in Electron Radiotherapy: A Monte Carlo Evaluation
Bibliographic record
Abstract
We investigated the dosimetric characteristics of the beam profile, relative dose at the central beam axis (CAX) and penumbra width at the depth of maximum dose ( ), varying with different sizes and thicknesses of a piece of lead (Pb) layer using different clinical electron beams. Monte Carlo simulations (EGSnrc‐based code) validated by measurements were used to calculate beam profiles at for different Pb shields and electron beams with energies of 4, 9 and 16 MeV. Pb layers with different thicknesses (2–8 mm) and sizes ( ) were placed at the center of an electron field on a Solid Water phantom. Beam profiles were determined at , and dosimetry under the Pb layer was studied. We found that 2 mm of Pb layer is adequate to provide 5 half‐value‐layer attenuation for the 4 MeV electron beams. However, for the 9 and 16 MeV electron beams, the relative dose at CAX and depends on the thickness and size of the Pb layer. The dosimetry of the beam profile under the Pb layer at depends on the penumbra at the edge of the layer, and beam attenuation varies with the thickness of the layer. The dosimetry of the profile also depends on the electron side‐scatter contributing to the CAX, and photon contamination produced by the Pb layer. The dosimetric data calculated by Monte Carlo simulations in this study provides useful information in selecting the Pb shield suitable for the protection of critical tissue in electron radiotherapy.
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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 | 0.000 |
| 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) | 0.002 | 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".