SU-F-T-379: Dosimetric Impacts of Topical Agents and Dressings On Skin in Radiotherapy
Bibliographic record
Abstract
Purpose: This study investigated the superficial dose enhancement in the application of topical agents, clinical materials (thermal mask and bolus) and dressings in megavoltage photon beam radiotherapy. Different topical skin agents, clinical materials and dressings were evaluated and compared for their skin dosimetric impacts on the patients during radiation treatment. Methods: Superficial dose enhancements, or percentage doses with and without the studying materials, were measured using the 6 MV (Field size = 10×10 cm2) photon beams produced by a Varian TrueBeam linear accelerator. Twelve topical agents, five dressings (dry and wet conditions) and three clinical materials were studied. A solid water phantom was used with a MOSFET dose detector (TN-1002RD, Thomson and Nielsen Electronic, Ottawa, Ontario, Canada) located under a 1-mm PMMA slab to measure the skin dose. The distance between the radiation source and phantom surface was set to 100 cm in all measurements. The topical agents were distributed evenly with 1.5 mm thickness using our specific sample holder on the phantom surface. Extrapolations were made of 0.5 mm thickness for the agents to provide meaningful clinical value. Results: By comparing surface doses without studying materials, it is found that no topical agents had superficial dose enhancement higher than the clinical materials namely, thermoplastic mask (128%), 5-mm Superflab™ bolus (158%) and 10-mm Superflab™ bolus (171%) regarding the same thickness. Superficial dose enhancement of dry dressing did not exceed 110.5%, while wet dressings produced higher dose enhancements (133% for wet Mepilex lite and 141% for wet Mepilex Ag transfer). Conclusion: It is concluded that the evaluated topical agents and dry dressings did not increase the superficial dose to a concerning level, even using excessive thickness in every fraction of radiation treatment. Wet dressings were found producing the bolus effect, but was still substantially less than applying a thin 5-mm bolus.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".