Clinical consequences of changing the sliding window IMRT dose rate
Bibliographic record
Abstract
Changing pulse repetition frequency or dose rate used for IMRT treatments can alter the number of monitor units (MUs) and the time required to deliver a plan. This work was done to develop a practical picture of the magnitude of these changes. We used Varian's Eclipse Treatment Planning System to calculate the number of MUs and beam-on times for a total of 40 different treatment plans across an array of common IMRT sites including prostate/pelvis, prostate bed, head and neck, and central nervous system cancers using dose rates of 300, 400 and 600 MU/min. In general, we observed a 4%-7% increase in the number of MUs delivered and a 10-40 second decrease in the beam-on time for each 100 MU/min of dose rate increase. The increase in the number of MUs resulted in a reduction of the "beam-on time saved". The exact magnitude of the changes depended on treatment site and planning target volume. These changes can lead to minor, but not negligible, concerns with respect to radiation protection and treatment planning. Although the number of MUs increased more rapidly for more complex treatment plans, the absolute beam-on time savings was greater for these plans because of the higher total number of MUs required to deliver them. We estimate that increasing the IMRT dose rate from 300 to 600 MU/min has the potential to add up to two treatment slots per day for each IMRT linear accelerator. These results will be of value to anyone considering general changes to IMRT dose rates within their clinic.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".