Total skin electron therapy vertical profiles measured using radiochromic film
Bibliographic record
Abstract
BACKGROUND: Vertical dose profiles of Total Skin Electron Therapy (TSET) electron fields are often measured using ionization chambers (ICs); however, resulting protocols are tedious and time consuming due to complex gantry arrangements, numerous point dose measurements and extra-cameral corrections. This inefficiency is reduced when using radiochromic film (RCF) dosimetry through simultaneous dose sampling and the elimination of IC-related measurement corrections. PURPOSE: To investigate the feasibility of RCF dosimetry for TSET vertical profile measurements and establish a novel RCF based vertical profile quality assurance protocol. METHODS: EBT-XD RCF on two matched linear accelerators (linacs) over 1.5 years. Absolute dose was quantified using a triple channel calibration method. Two IC profiles were collected for comparison to RCF profiles. Twenty-one archived IC measured profiles from two different matched linacs from 2006 to 2011 were analyzed. Inter- and intra-profile dose variability was compared between dosimeters. The time required for the RCF and IC protocols was compared. RESULTS: RCF measured inter-profile variability ranged from 0.66%-5.16% and 1.30%-3.86% for the two linacs. A 0.2%-5.4% inter-profile variability was observed for archived IC measured profiles. RCF measured intra-profile variability ranged from 10.0%-15.8%; six of 31 profiles exceeded the EORTC ± 10% limit. Archived IC measured profiles exhibited lower intra-profile variability (4.5%-10.4%). RCF and IC measured profiles agreed in the center of the field; however, RCF doses measured 170-179 cm above the TSET treatment box base were ∼7% greater. Modification to the RCF phantom eliminated this discrepancy, resulting in comparable intra-profile variability and agreeance with the ±10% limit. Measurement times were reduced from 3 h (IC protocol) to 30 min (RCF protocol). CONCLUSIONS: RCF dosimetry improves protocol efficiency. RCF has been established as a valuable dosimeter for TSET vertical profile quantification when compared to ICs as the gold standard.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".