A new DOSXYZnrc method for Monte Carlo simulations of 4D dose distributions
Bibliographic record
Abstract
The purpose of this study is to present a novel method for generating Monte Carlo 4D dose distributions in a single DOSXYZnrc simulation. During a standard simulation, individual energy deposition events are summed up to generate a 3D dose distribution and their associated temporal information is discarded. This means that in order to determine dose distributions as a function of time, separate simulations would have to be run for each interval of interest. Consequently, it has not been clinically feasible until now to routinely perform Monte Carlo simulations of dose rate, time-resolved dose accumulation, or electronic portal imaging devices (EPID) cine-mode images for volumetric modulated arc therapy (VMAT) plans. To overcome this limitation, we modified DOSXYZnrc and defined new input and output variables that allow a time-like parameter associated with each particle history to be binned in a user-defined manner. Under the new code version, computation times are the same as for a standard simulation, and the time-integrated 4D dose is identical to the standard 3D dose. We present a comparison of scintillator measurements and Monte Carlo simulations for dose rate during a VMAT beam delivery, a study of dose rate in a VMAT total body irradiation plan, and simulations of transit (through-patient) EPID cine-mode images.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".