Poster - Thur Eve - 56: Design of Quality Assurance Methodology for VMAT system on Agility System equipped with CVDR
Bibliographic record
Abstract
The aim of this study was to analyze the feasibility of designing comprehensive QA plans using iComCAT for Elekta machines equipped with Agility multileaf collimator and continuously variable dose rate. Test plans with varying MLC speed, gantry speed, and dose rate were created and delivered in a controlled manner. A strip test was designed with three 1 cm MLC positions and delivered using dynamic, StepNShoot and VMAT techniques. Plans were also designed to test error in MLC position with various gantry speeds and various MLC speeds. The delivery fluence was captured using the electronic portal-imaging device. Gantry speed was found to be within tolerance as per the Canadian standards. MLC positioning errors at higher MLC speed with gravity effects does add more than 2 mm discrepancy. More tests need to be performed to evaluate MLC performance using independent measurement systems. The treatment planning system with end-to-end testing necessary for commissioning was also investigated and found to have >95% passing rates within 3%/3mm gamma criteria. Future studies involve performing off-axis gantry starshot pattern and repeating the tests on three matched Elekta linear accelerators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".