Poster — Wed Eve—35: The Accuracy of Static and Dynamic Asymmetric Field Dose Calculations with Varian's Anisotropic Analytical Algorithm
Bibliographic record
Abstract
The Anisotropic Analytical Algorithm (AAA) of the Varian Eclipse treatment planning system is the newest of the Eclipse photon dose calculation algorithms for all static and dynamic fields. The algorithm has gone through a number of changes and version releases since its introduction in July 2005. The purpose of this paper is to report specifically on a careful assessment of the dose accuracy of asymmetric static and dynamic fields. The accuracy is assessed by comparing Eclipse (v8.1.18) dose calculations with 2D dose measurements acquired with a commercial ion chamber array. A series of half‐field and quadrant‐field tests illustrate that significant discrepancies between calculation and measurement occur when asymmetric fields contain either a static or a dynamic MLC. Dose differences as large as 5% were observed and gamma map analysis showed large regions of gamma failure (3% and 3 mm criteria). The dose discrepancies are traced to an error in the second‐source contribution for static or dynamic asymmetric fields containing an MLC. With the February 2009 release of AAA version 8.6, Varian reports that the second‐source calculation error illustrated in this paper (v8.1.18) has been corrected. This paper shows test results that confirm the correction has been made for the new version of 8.6. Care should be taken when using versions of AAA prior to 8.6.14 for any asymmetric field containing an MLC.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".