Dosimetry limitations and pre-treatment dose profile correction for sliding window IMRT
Bibliographic record
Abstract
*Corresponding author: Dr. Grigor Grigorov, Medical Physics Department, Grand River Regional, Cancer Center, PO Box 9056, 835 King St West, Kitchener, ON, Canada N2G 1G3. E-mail: grigor.grigorov@grhosp.on.ca Background: This work investigated the dosimetry limitations of the random and systematic uncertainties of sliding window (SW) intensity modulated radiation therapy (IMRT). Materials and Methods: A Varian 21EX linear accelerator, Pinnacle3 treatment planning system and radiographic film dosimetry was used. The limitations of the SW were studied using beam modulation ranging from 2 to 100 MU/beam, DR from 100 to 600 MU min-1, LV from 1 to 5 cm s-1 and field size up to 12 × 12 cm2. The random and systematic errors were investigated using clinical and flat beams, as well as beams of high profile modulation including linear, exponential, and sinusoidal profiles. Results: The leading edge and plateau of the SW profiles have a significant deformation for higher DR and for beams of 10 MUs irradiated by a DR from 100 to 600 MU min-1 and LV from 1 to 5 cm s-1. After the proposed correction, an average difference < 0.5% for clinical profiles was measured for beams irradiated with DR = 600 MU min-1 and LV= 5 cm s-1. It was concluded that this correction methodology may serve as a pre-treatment Quality Assurance tool for SW IMRT beams. Iran. J. Radiat. Res., 2010; 8 (2): 6174
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".