Optimum radiation source for radiation therapy of skin cancer
Bibliographic record
Abstract
Several different applicators have been designed for treatment of skin cancers, such as scalp, hand, and legs using Ir-192 HDR brachytherapy sources (IR-HDRS), miniature electronic brachytherapy sources (eBT), and external electron beam radiation therapy (EEBRT). Although, all of these methodologies may deliver the desired radiation dose to the skin, but the dose to the underlying bone may become the limiting factor for selection of the optimum treatment technique. In this project, dose to the underlying bone has been evaluated as a function of the radiation type, thickness of the bone, and thickness of the soft tissue on top of bone, assuming the same radiation dose delivery to the skin. These evaluations are performed using Monte Carlo (MC) simulation technique with MCNP5 code. The results of these investigations indicate that, for delivery of the same skin dose with a 50keV eBT, 4 MeV or 6 MeV EEBRT techniques, the average doses received by the underlying bones are 5.31, 2, or 1.75 times the dose received from IR-HDRS technique, respectively. These investigations indicate that, for the treatment of skin cancer condition with bone immediately beneath skin, the eBT technique may not be the most suitable technique, as it may lead to excessive bone dose relative to IR-HDRS and 6 MeV or 4 MeV electron beams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".