Sci—Thur PM: Planning & Delivery — 05: Evaluation of dose difference between VMAT plans with and without jaw tracking
Bibliographic record
Abstract
The goal of this study is to quantify the dose difference between VMAT plans calculated with and without jaw tracking. In this study the sites were chosen so that there would be jaw tracking in the direction perpendicular to the leaf motion (Y jaws). For VMAT plans without jaw tracking in the Y direction there is additional dose (over the leaf transmission) leaking through abutting leaves that can't be moved out of the field and therefore move across the treatment field during delivery. VMAT plans for four head and neck patients with concurrent boost and three pelvis patients with concurrent prostate boost were generated using jaw tracking. A code was written in Matlab to convert each VMAT plan with jaw tracking (JT plan) to a VMAT plan with static jaws (SJ plan). The ST plan dose distribution was then recalculated and compared to the JT plan dose. VMAT plans with static jaws leave an additional dose trail compared to VMAT plans with jaw tracking. Between 6.7 and 230 cc of the SJ plans received an additional 2% of the prescription dose when compared to the JT plans and 0.5 to 30.1 cc received an additional 4% of the prescription dose. The additional dose trail left by the 2 arcs VMAT plans was less than the 1 arc VMAT for most plans presented in this study. This additional dose is given to normal tissues and/or critical structures surrounding the PTV.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".