Poster - Thur Eve - 47: Monte Carlo Simulation of Scp, Sc and Sp
Bibliographic record
Abstract
The in-water output ratio (Scp), in-air output ratio (Sc), and phantom scattering factor (Sp) are important parameters for radiotherapy dose calculation. Experimentally, Scp is obtained by measuring the dose rate ratio in water phantom, and Sc the water Kerma rate ratio in air. There is no method that allows direct measurement of Sp. Monte Carlo (MC) method has been used to simulate Scp and Sc in literatures, similar to experimental setup, but no MC direct simulation of Sp available yet to the best of our knowledge. We propose in this report a method of performing direct MC simulation of Sp. Starting from the definition, we derived that Sp of a clinical photon beam can be approximated by the ratio of the dose rates contributed from the primary beam for a given field size to the reference field size. Since only the primary beam is used, any Linac head scattering should be excluded from the simulation, which can be realized by using the incident electron as a scoring parameter for MU. We performed MC simulations for Scp, Sc and Sp. Scp matches well with golden beam data. Sp obtained by the proposed method agrees well with what is obtained using the traditional method, Sp=Scp/Sc. Since the smaller the field size, the more the primary beam dominates, our Sp simulation method is accurate for small field. By analyzing the calculated data, we found that this method can be used with no problem for large fields. The difference it introduced is clinically insignificant.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".