Po‐Thur Eve General‐27: Analytical model for electron arc beam output determination using an Elekta SL‐25 linear accelerator
Bibliographic record
Abstract
The clinical implementation of electron arc therapy requires a large amount of measured dosimetric data. All necessary measurements were done on an Elekta SL‐25 with a 10 MeV electron beam. Beam output and PDD measurements were performed in five different cylindrical acrylic phantoms using TLDs. 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. 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 field width at isocentre (w). Curve fitting of the complete set of beam output data 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 calculated points are within a ± 5% error range. The MU calculation then becomes straightforward, without the need of measuring each clinical case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".