Poster — Thur Eve — 42: Dynamic delivery quality assurance on Elekta linacs
Bibliographic record
Abstract
Introduction Recently, Elekta linacs have gained the capability to deliver dynamic fields, such as sliding window IMRT or VMAT fields. Because of the potential complexity of such delivery modes, linac QA and patient-specific QA are of prime importance. The aim of this paper is to explain the fundamental concepts of this new mode of operation on Elekta linacs as well as to introduce the linear-α slit, a novel dynamic QA sequence which allows the performance of the delivery system to be objectively aassessed against dosimetric measurements. In the Elekta dynamic mode, dose rate can only take a limited number of discontinuous values. We have used the uniform slit, a simple slit moving at a constant speed across the field to verify how the linac selects leaf speed and dose rate for a given MU setting. Based on this, we expose the principles behind the linear-α slit, which exhibits two main characteristics: 1) it exercises the linac over a range of dose rates and 2) it produces a dose distribution which is theoretically equivalent to the uniform slit. Discrepancies between measurements of the linear-α slit and the uniform slit directly reflects problems with the delivery. The linear-α slit has been recently introduced in our routine monthly linac QA. We hope it will nicely complement patient-specific QA, and exising linac-QA.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".