Poster ‐ 53: Improving inter‐linac DMLC IMRT dose precision by fine tuning of MLC leaf calibration
Bibliographic record
Abstract
Purpose: To develop a method to improve the inter‐linac precision of DMLC IMRT dosimetry. Methods: The distance between opposing MLC leaf banks (“gap size”) can be finely tuned on Varian linacs. The dosimetric effect due to small deviations from the nominal gap size (“gap error”) was studied by introducing known errors for several DMLC sliding gap sizes, and for clinical plans based on the TG119 test cases. The plans were delivered on a single Varian linac and the relationship between gap error and the corresponding change in dose was measured. The plans were also delivered on eight Varian 2100 series linacs (at two institutions) in order to quantify the inter‐linac variation in dose before and after fine tuning the MLC calibration. Results: The measured dose differences for each field agreed well with the predictions of LoSasso et al. Using the default MLC calibration, the variation in the physical MLC gap size was determined to be less than 0.4 mm between all linacs studied. The dose difference between the linacs with the largest and smallest physical gap was up to 5.4% (spinal cord region of the head and neck TG119 test case). This difference was reduced to 2.5% after fine tuning the MLC gap calibration. Conclusions: The inter‐linac dose precision for DMLC IMRT on Varian linacs can be improved using a simple modification of the MLC calibration procedure that involves fine adjustment of the nominal gap size.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".