Poster — Thur Eve — 19: Performance assessment of a 160‐leaf beam collimation system
Bibliographic record
Abstract
In this study, the performance of the new beam collimation system with 160 leaves, each with a 5 mm leaf width projected at isocenter, is evaluated in terms of positional accuracy and plan/delivery quality. Positional accuracy was evaluated using a set of static and dynamic MLC/jaw delivery patterns at different gantry angles, dose rates, and MLC/jaw speeds. The impact on IMRT plan quality was assessed by comparing against a previous generation collimation system using the same optimization parameters, while delivery quality was quantified using a combination of patient‐specific QA measurements with ion chambers, film, and a bi‐planar diode array. Positional accuracy for four separate units was comparable. The field size accuracy, junction width, and total displacement over 16 cm leaf travel are 0.3 ± 0.2 mm, 0.4 ± 0.3 mm, and 0.5 ± 0.2 mm, respectively. The typical leaf minor offset is 0.05 ± 0.04 mm, and MLC hysteresis effects are 0.2 ± 0.1 mm over 16 cm travel. The dynamic output is linear with MU and MLC/jaw speed, and is within 0.7 ± 0.3 % of the planning system value. Plan quality is significantly improved both in terms of target coverage and OAR sparing due, in part, to the larger allowable MLC and jaw speeds. γ‐index pass rates for the patient‐specific QA measurements exceeded 97% using criteria of 2%/2 mm. In conclusion, the performance of the Agility system is consistent among four separate installations, and is superior to its previous generations of collimation systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".