SU-FF-T-209: Electron Arc Therapy with An Elekta SL-25 10 MeV Beam Using a Dedicated Short Applicator
Bibliographic record
Abstract
Purpose: To implement the electron arc therapy with an Elekta SL-25 10 Mev beam using a dedicated short applicator. Method and Materials:. All measurements were done on an Elekta SL-25 with a 10 MeV electron beam. Radial PDD were characterized for 4 different cylindrical acrylic phantoms using TLDs and Kodak X-Omat-V radiographic films The phantom radius (di) ranged between 5.1 and 15.2 cm, the total arc angles (α) varied between 60° and 160° and the number of monitor units (MU) per degree between 0.5 and 7. The field with at isocenter (w) was varied between 3.7 and 9.8 cm. Based on this data bank, an analytical model was developed for monitor unit (MU) calculation. This model estimates arc beam output at the depth of maximum dose (dmax) as a function of di and α for a given w. A verification of the model precision was done with a new cylindrical phantom. Results: Curve fitting of the complete set of beam output data with w fixed at 7.3 cm was done with an asymptotic relationship between the dose rate at dmax and the inverse square of di. The dependence of the beam output on α was introduced by assuming an explicit function of α for each parameter of the model. Results show that the calculated beam output data is a good approximation for all measured data for all phantoms and arc angles: 89% of the calculated values are within ± 3% of the measured ones and all points are within a ± 5% error range. Conclusion: From our results, clinical implementation of the electron arc therapy technique is possible and should be facilitated by the use of our predictive model in the treatment planning process.
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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 | 0.000 | 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 | 0.001 | 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".