SU‐FF‐T‐391: IC Profiler Study: The End of Water Tank Measurements for Monthly QA?
Bibliographic record
Abstract
Purpose: To investigate the possibility of using the IC Profiler (Sun Nuclear Corporation) for monthly QA of linear accelerators. Method and Materials: Photon and electron profiles measured with the IC Profiler, consisting of 251 ionization chambers located along the X, Y and diagonal axes, were compared to the profiles measured in water with an IC10 ionisation chamber. Three different methods were used for energy measurement of the photon and electron beams: 1) different thicknesses of solid water slab on the IC Profiler, 2) an acrylic continuous energy wedge and 3) a step wedge made of solid water. For the last two methods, the measured PDD equivalent curve was corrected for the change in profiles in order to remove the profile effect. All photon measurements were performed at SSD=75 cm with a 30×30 cm2 field size in order to be equivalent to a 40×40 cm2 at the isocenter. The electron measurements were done at SSD=100 cm with a 25×25 cm2 field size. Results: The agreement between symmetry and flatness measurements with the IC Profiler and the water tank was very good: maximum relative difference of −0.7% for 6 MV and 25 MV photon beams and −1.3% for electron beams (from 4 MeV to 22 MeV). Energy measurement using methods 2 and 3 was very fast and seemed to be as sensitive to an energy change as method 1. Method 2 gives a linear interpolation of the relative dose as function of the depth whereas method 3 gives the relative dose averaged over 10 detectors at 4 different depths. Conclusion: The IC Profiler is well adapted to the measurement of photon and electron profiles. Three different methods were tested for energy determination and more measurements are needed to establish the most accurate one.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".