SU-FF-T-669: A Comparison of MLC Demands Between Dosimetrically Equivalent RapidArcTM and Conventional IMRT Deliveries
Bibliographic record
Abstract
Purpose: To compare the MLC motion requirements of dosimetrically comparable RapidArc™ and conventional IMRT plans. Method and Materials: A program was written to read the MLC control point positions from DICOM RT plan files and calculate for each leaf the total distance travelled, the velocity for each control point and the number of direction changes. Pairs of RapidArc™ and conventional IMRT plans were generated such that the resulting distributions were dosimetrically as close as possible. These plan pairs were analyzed for the requirements made on the MLC system and compared. Results: It was observed that the average total distance travelled and the maximum velocity for each active MLC leaf was about equal for the two types of deliveries. However the average number of directional changes for each active leaf was 10 times greater for the RapidArc™ delivery. Conclusions: Although the total distance and velocities of the MLC leaves are comparable, the ten fold increase in MLC directional changes throughout the RapidArc™ delivery could increase the amount of wear and service required for the MLC system. These additional directional changes could also increase interlocks due to the MLC decoder backlash errors requiring increased MLC initialization frequency.
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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 | 0.004 |
| 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) | 0.003 | 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".