Sci—Wed PM: Delivery—08: Monte Carlo Based RapidArc QA Using LINAC Log Files
Bibliographic record
Abstract
Purpose/Objective(s): To present our Monte Carlo based RapidArc quality assurance (QA) process to validate both the dose calculation and dynamic beam delivery accuracy using the planning MLC control files and the post‐delivery MLC diagnostic files. Materials/Methods: Ten clinically acceptable RapidArc treatment plans were generated with a clinical version of the planning system for various tumor sites. Monte Carlo dose calculations were performed in a water equivalent phantom for each plan using both DynaLog files and the planning control (DVA) files. Results were compared to measurements using a calibrated Farmer ionization chamber with an active volume of . Comparison of RapidArc and Monte Carlo 3D doses was performed using a 3 dimensional Gamma‐factor analysis with a 3%/3mm DTA criteria. A thorough analysis of the DynaLog files was performed to evaluate the treatment delivery accuracy. Results: Good agreement was observed between chamber measurements and MC dose calculations and between RapidArc and MC dose distributions with Gamma values below 1 in over 90% of the points considered for all plans. The analysis of the MLC DynaLog files indicated that the leaf position errors were lower than 1 mm in more than 94% of the time with none above 2.5 mm and that few beam hold‐offs occurred Conclusions The accuracy and flexibility of our Monte Carlo based RapidArc QA system was demonstrated. Good machine performance and accurate dose distributions delivery of RapidArc plans was observed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.031 |
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".