Sci—Fri PM: Delivery — 05: A complete set of VMAT machine‐specific QA tests using EPID
Bibliographic record
Abstract
During a VMAT (Volumetric Modulated Arc Therapy) radiation delivery, various machine parameters (gantry position and speed, MLC leaves position, dose rate, etc) have to be highly synchronized and coordinated. To ensure that all of the machine components are effectively operating together requires unique machine-specific quality assurance (QA) tests. The purpose of this work is to describe a complete set of VMAT machine-specific QA tests using Electronic Portal Imaging Device (EPID). To address the need for VMAT machine-specific QA tests, Varian has recommended 6 EPID-based tests for commissioning and QA of RapidArc™ capable machines. Recent studies have identified that these Varian tests do not define a complete set of tests needed for adequate commissioning and machine-specific QA of RapidArc machines. The goal of this work is twofold: a) to report and discuss the results of the original 6 Varian's EPID-based tests, and b) to extend the original set by adding 2 more EPID-based tests, which results in a more complete and comprehensive set of tests. We have demonstrated that the Varian EPID-based QA tests can be used to verify that the synchronization of Dose Rate, Gantry Speed and MLC Leaves speed is generally to within 1.5% during a RapidArc delivery. Also, the gantry rotation does not have any significant effect on MLC leaves positional accuracy during a RapidArc delivery. Finally, we have introduced a new EPID-based QA test, which can be used to verify that the angular accuracy of the gantry position during a RapidArc delivery is to within ∼1 degree.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.025 |
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".