A comparison of the JO-IMRT dose distribution calculated by the SOURCE 8 and 20 in the DOSXYZnrc for head-and-neck cancer
Bibliographic record
Abstract
Background: The purpose of this study is to compare SOURCE 8 and 20 in the EGSnrc-based DOSXYZnrc Monte Carlo code for Jaws-only intensitymodulated radiation therapy (JO-IMRT) dose distribution, and demonstrate the advantage of SOURCE 20 to SOURCE 8 to treat head-and-neck cancer. Materials and Methods:The clinical photon beams of the HPD Siemens Primus linear accelerator simulated using the BEAMnrc code and then verified by measurement.The phase-space files generated by the BEAMnrc code were used as an input for the DOSXYZnrc to calculate the JO-IMRT dose distributions of patients (in form of CT images) using the SOURCES 8 and 20.The isodose distribution on slices, DVH and gamma index (3%/3 mm, 2%/2 mm, and 1%/1 mm) were compared with Monte Carlo and treatment planning system (TPS) results.Furthermore, the efficient computation of dose distributions such as time running, working load and uncertainty error calculation also considered for evaluation.Results: JO-IMRT dose distributions calculated by SOURCE 8 and 20 compared with the Prowess Panther TPS.The time running, working load based on SOURCE 20 were significantly less than SOURCE 8. Futhermore, the uncertainty calculation based on SOURCE 20 was also significantly less than SOURCE 8. Conclusion: The JO-IMRT dose distributions calculated by SOURCE 20 are more efficient than SOURCE 8.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".