Sci-Sat AM(1): Planning - 10: Evaluation of a New Commercial Monte-Carlo Treatment Planning System for Electrons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has long been understood that the Monte Carlo (MC) method is the most effective means for accurately computing the dose delivered by clinical electron beams. Every commercial implementation of the MC method involves design compromises and the possibility of error. It is important, therefore, that each implementation is independently validated under conditions similar to those found in the clinic. In this abstract, we present the initial stages of validation for the XiO electron Monte Carlo (XiO eMC) software, a new treatment planning system for electron beams developed and commercialized by CMS incorporated. In this abstract we present a limited set of comparisons of calculated and experimental data for homogeneous water phantoms and for a 3D heterogeneous phantom meant to approximate the geometry of a trachea and spine. All Monte Carlo calculated and measured output factors agree within the estimated standard error for standard and extended SSD for open applicators and cerrobend cutouts with the exception of the smallest cutout size (2×2cm2) for 17 MeV at extended SSD. We also found good agreement between calculated and experimental depth dose curves and dose profiles. Dose calculations in heterogeneous phantoms are also in a very good agreement with measurements, given an estimated positional uncertainty of ±0.1cm in the depth direction, and provided that appropriate calculation voxel sizes are used for a given geometry. Acknowledgment: The authors would like to acknowledge the excellent technical support provided by Dr. J C Satterthwaite of Elekta CMS Software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it