SU‐E‐T‐426: Assessment of the Limitations of Intensity Modulated Radiation Therapy Quality Control Procedure
Bibliographic record
Abstract
Purpose: To determine the impact of dose delivery errors due to multileaf collimator bank offset and gantry angle offset on the resulting gamma value of an IMRT Quality Control (QC) test as determined by a planar 2D dosimeter. Also to determine the impact of the planar 2D dosimeter array resolution on the gamma. Methods: Systematic errors such as MLC leaf bank offset and gantry angle offset were numerically introduced in IM verification plans. The dose received by a phantom was calculated using anisotropic analytical algorithm with a grid size of 1 mm. A corona plane (30×30 cm2) was selected for each modified plan and compared t original using the geometric gamma approach in absolute or relative using a 3%/3 mm criteria. The plans were then irradiated in a phan composed of 20×20 cm2 slabs of plastic water. The phantom was im using a CT scan and imported into the treatment planning system Ec (Varian inc.). A radiochromic EBT2 (ISP Corp. inc.) film was placed depth of 4 cm. Results: Absolute dose measurements are much m sensitive to MLC bank offsets than relative measurements. In con modifying the gantry angle by 1 degree has little effect on the relativ absolute gamma value. There is a clear increase in the variance of gamma value as the detector spacing is increased beyond 2–3 Conclusions: Absolute dose measurements should be performed maximize QC sensitivity. Ideally, the detector spacing in a 2D planar a should be less than 2–3 mm in order to minimize the uncertainty on gamma. This work has been supported by the Ministère de la Santé et des Ser Sociaux and Natural Sciences and Engineering Research Council Disco Grant No. 357402.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".