The comparison between 6 MV Primus LINAC simulation output using EGSnrc and commissioning data
Bibliographic record
Abstract
Abstract Introduction Monte Carlo calculation method is considered to be the most accurate method for dose calculation in radiotherapy. The purpose of this research is comparison between 6 MV Primus LINAC simulation output with commissioning data using EGSnrc and build a Monte Carlo geometry of 6 MV Primus LINAC as realistically as possible. The BEAMnrc and DOSXYZnrc (EGSnrc package) Monte Carlo model of the LINAC head was used as a benchmark. Methods In the first part, the BEAMnrc was used for the designing of the LINAC treatment head. In the second part, dose calculation and for the design of 3D dose file were produced by DOSXYZnrc. The simulated PDD and beam profile obtained were compared with that calculated using commissioning data. Good agreement was found between calculated PDD (1·1%) and beam profile using Monte Carlo simulation and commissioning data. After validation, TPR 20,10 , TMR and S p values were calculated in five different field. Results Good agreement was found between calculated values by using Monte Carlo simulation and commissioning data. Average differences for five field sizes in this approach is about 0·83% for S p . for TPR 20,10 differences for field sizes 10×10 cm 2 is 0·29% and for TMR in five field sizes, the average value is ~1·6%. Conclusion In conclusion, the BEAMnrc and DOSXYZnrc codes package have very good accuracy in calculating dose distribution for 6 MV photon beam and it can be considered as a promising method for patient dose calculations and also the Monte Carlo model of primus linear accelerator built in this study can be used as method to calculate the dose distribution for cancer patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".