Dose to radiation therapists from activation at high‐energy accelerators used for conventional and intensity‐modulated radiation therapy
Bibliographic record
Abstract
The increased beam-on times which characterize intensity-modulated radiation therapy (IMRT) could lead to an increase in the dose received by radiation therapists due to induced activity. To examine this, gamma ray spectrometry was used to identify the major isotopes responsible for activation at a representative location in the treatment room of an 18 MV accelerator (Varian Clinac 21EX). These were found to be 28Al, 56Mn, and 24Na. The decay of the dose rate measured at this location following irradiation was analyzed in terms of the known half-lives to yield saturation dose rates of 9.6, 12.4, and 6.2 microSv/h, respectively. A formalism was developed to estimate activation dose (microSv/week) due to successive patient irradiation cycles, characterized by the number of 18 MV fractions per week, F, the number of MU per fraction, M, the in-room time between fractions, td (min), and the treatment delivery time t'r (min). The results are represented by the sum of two formulas, one for the dose from 28Al 1.8 x 10(-3) F M (1-e(-03t'(r))/t'r and one for the dose from the other isotopes approximately 1.1 x 10(-6) F(1.7) Mt(d). For conventional therapy doses are about 60 microSv/week for an 18 MV workload of 60,000 MU/week. Irradiation for QA purposes can significantly increase the dose. For IMRT as currently practiced, lengthy treatment delivery times limit the number of fractions that can be delivered per week and hence limit the dose to values similar to those in conventional therapy. However for an IMRT regime designed to maximize patient throughput, doses up to 330 microSv/week could be expected. To reduce dose it is recommended that IMRT treatments should be delivered at energies lower than 18 MV, that in multienergy IMRT, high-energy treatments should be scheduled in the latter part of the day, and that equipment manufacturers should strive to minimize activation in the design of high-energy accelerators.
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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 | 0.000 | 0.000 |
| 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 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".