Calculation of Jaws-only IMRT (JO-IMRT) dose distributions based on the AAPM TG-119 test cases using Monte Carlo simulation and Prowess Panther treatment planning system
Bibliographic record
Abstract
The aim of this study is to calculate the JO-IMRT dose distributions based on the AAPM TG-119 using Monte Carlo (MC) simulation and Prowess Panther treatment planning system (TPS) (Panther, Prowess Inc., Chico, CA). JO-IMRT dose distributions of AAPM TG-119 were calculated by the TPS and were recalculated by MC simulation. The DVHs and 3D gamma index using global methods implemented in the PTW-VeriSoft with 3%/3 mm were used for evaluation. JO-IMRT dose distributions calculated by TPS and MC were matched the TG-119 goals. The gamma index passing rates with 3%/3 mm were 98.7% for multi-target, 96.0% for mock prostate, 95.4% for mock head-and-neck, and 96.6% for C-shape. The dose in the planning target volumes (PTV) for TPS was larger than that for the MC. The relative dose differences in D99 between TPS and MC for multi-target are 1.52%, 0.17% and 1.40%, for the center, superior and inferior, respectively. The differences in D95 are 0.16% for C-shape; and 0.06% for mock prostate. Mock head-and-neck difference is 0.40% in D99. In contrast, the organ curve for TPS tended to be smaller than MC values. JO-IMRT dose distributions for the AAPM TG-119 calculated by the TPS agreed well with the MC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".