Poster - Thurs Eve-05: An assessment of PDDs and outputs predicted by a Monte Carlo-based treatment planning system for electron beams
Bibliographic record
Abstract
Monte Carlo simulation is currently considered to be the most accurate method of calculating dose distributions for electron beam therapy, and commercial treatment planning software using simplified macro Monte Carlo is available for electron treatment planning. In this work, Eclipse V8.1.18 is being investigated in preparation for the clinical use of CT-based electron treatment planning. Water tank measurements of percentage depth doses (PDDs) and absolute outputs at depth of maximum dose (Zmax) under different geometric conditions are compared to the results calculated by Eclipse. The measurements are carried out for a range of electron energies (6, 9, 12, and 16 MeV) for the standard open field (10×10 cm2) and for circular cutouts (2, 3, and 6 cm diameters) at SSD of 100 cm. In addition, extended SSDs (105 and 110 cm) and oblique beam incident (gantry 345 degree) for the open field and 3 cm diameter cutout are measured and compared to Eclipse. For PDDs, the results predicted by Eclipse are generally acceptable, falling mostly within 5% of those measured in water. For output, the results predicted by Eclipse are similar, falling mostly within 3% of those measured in water. We observed the greatest differences between Eclipse and measurements near the water surface and in high dose gradients for PDDs. A similar observation is noted for a small field in the case of outputs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".