Analytical representation of enhanced dynamic wedge factors for symmetric and asymmetric photon fields
Bibliographic record
Abstract
The Enhanced Dynamic Wedge (EDW) presents many advantages over the physical wedge. However, in order to calculate monitor units (MUs) necessary to deliver a certain dose at a certain point, EDW factors (EDWFs) need to be determined. In this work, based on analysis of the golden segmented treatment table (GSTT) and the MU fraction model, an empirical analytic formula has been developed to calculate EDW factors for symmetric and asymmetric fields. This formalism is an extension of the MU fraction model. However in comparison with previous studies [J. P. Gibbons, Med. Phys. 25, 1411-1418 (1998) and M. Miften et al., Med. Dosim. 25, 81-86 (2000)], this formula is simpler, and easier to use. It is applicable to EDW fields of different sizes, wedge angles and different photon energies. For 6 and 18 MV beams from a Varian 21EX accelerator with 7 EDW angles (Varian Oncology Systems, Palo Alto, CA), more than 250 measured EDWFs for symmetric and asymmetric fields with different off-axis distances and field sizes were compared with model calculations. Results show that 80% and 98% of calculated EDWFs match corresponding measured values to within 0.5% and 1.0%, respectively, the maximum deviation being 1.3%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".