Poster — Thur Eve — 14: Pretreatment IMRT QA Program for Low‐Dose Control Points Based on Dynamic Noise Correction Using a 2D Matrix of Ionization Chambers
Bibliographic record
Abstract
We developed a pre‐treatment intensity modulated radiation therapy (IMRT) QA program for verification of low‐dose control points registered by a 2D matrix of ionization chambers (ICs). The program eliminates the dose error caused by the induced reading or noise for every chamber electrically. It was found that the low‐dose deviation was in the rate of 20–40% outside the field (i.e. in the volume of the normal tissue). Moreover, the voxel‐to‐voxel dose was found to have non‐uniform deviation, which increased linearly with time. The rates of the electrically induced chamber readings were 0.3–1.3 cGy per 0.5 minute, when a beam of 100 monitor units using dose rate equal to 300 MU/min with consideration of the time for the leaf motion, was used in the irradiation with time > 20 s. This means that if low dose point (3–5 cGy) is registered by an IC of the highest induced readings (1.3 cGy), the deviation between the planned and delivered doses would be in the rate of 40−25%. Comparison of the fluence maps may be affected by the non‐uniformly induced dose readings. A field function depending on the beam irradiation time to correct the registered dose profile is therefore needed. In this study, a pre‐treatment IMRT QA program fully based on absolute dose measurements has been established. The program includes a comparison between the calculated and measured doses. The electrically induced readings of the 2D matrix were subtracted from the beam dose map to correct the measurements.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".