Comparison of linear energy transfer scoring techniques in Monte Carlo simulations of proton beams
Bibliographic record
Abstract
Monte Carlo (MC) simulations are commonly used to study linear energy transfer (LET) distributions in therapeutic proton beams. Various techniques have been used to score LET in MC simulations. The goal of this work was to compare LET distributions obtained using different LET scoring techniques and examine the sensitivity of these distributions to changes in commonly adjusted simulation parameters. We used three different techniques to score average proton LET in TOPAS, which is a MC platform based on the Geant4 simulation toolkit. We determined the sensitivity of each scoring technique to variations in the range production thresholds for secondary electrons and protons. We also compared the depth-LET distributions that we acquired using each technique in a simple monoenergetic proton beam and in a more clinically relevant modulated proton therapy beam. Distributions of both fluence-averaged LET (LETΦ) and dose-averaged LET (LETD) were studied. We found that LETD values varied more between different scoring techniques than the LETΦ values did, and different LET scoring techniques showed different sensitivities to changes in simulation parameters.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".