Dual-Fields Rotational Total Skin Electron Therapy: Investigation and Implementation
Bibliographic record
Abstract
Purpose: To present a protocol of a dual-field rotational (DFR) total skin electron therapy (TSET) and to provide an assessment of clinical implementation, dosimetry properties, and skin dose evaluation. Methods and Materials: The DFR-TSET combined the Stanford 6-field and McGill rotational methods. Dual 6 MeV electron beams in high dose total skin electron mode were used for DFR-TSET on a commercial linac. Beam profiles and dosimetric properties were measured using solid phantoms. The dose rate at expanded source-to-surface distance (SSD) was a combination of static rate and rotational rate. In vivo dosimetry of patient skin was performed on patients’ skin using film, metal oxide semiconductor field-effect transistors (MOSFET), and optically stimulated luminescent dosimeters (OSLD). Results: Dual field rotational total skin electron therapy exhibited good (≤±10%) uniformity in the beam profiles in the vertical direction at an extended SSD of 332 cm with a gantry angulation of ±20˚ deviated from the horizontal direction. In-vivo measurements confirmed acceptable uniformity of the patients’ total body surfaces and revealed anatomically self-blocked or shielded areas where underdosing occurred. Conclusions: The clinical implementation of DFR-TSET effectively utilizes the special mode on a linac. This technique provides short beam-on times, uniform dose distribution, large treatment field, and reduced dose of x-ray contamination to the patients. In-vivo measurements indicate satisfactory delivery and dose uniformity of the prescribed dose. Electron boost fields are recommended at normal SSDs to address underdosed areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".