Dose calculation along the nonwedged direction for externally wedged beams: Improvement of dosimetric accuracy with comparatively moderate effort
Bibliographic record
Abstract
Wedge filters ideally modify photon intensities only in one direction. However, in the other, the "nonwedged" direction, the intensity is affected too; it usually decreases with increasing off-axis distance. For external wedges on a particular treatment machine (Varian Clinac 2100C) and 6 MV photons, for example, this decrease is as big as 8%, depending on wedge angle and material, off-axis distance, and phantom depth. We present a way to account for this effect in prescriptions to points off-center in the nonwedged direction. The goal was to minimize the amount of additional data required for this purpose, without unduly compromising the final prescription accuracy. We measure the effective attenuation coefficients in narrow beam geometry for the wedge materials (lead and steel) as a function of thickness and off-axis angle, and the corresponding attenuation in water, again as a function of wedge material thickness and off-axis angle. The data allow us to extract a correction factor for off-axis distances in the nonwedged direction. Neglecting the contribution of scattered radiation and using primary beam data only, shortens data acquisition and simplifies calculations, but still yields surprisingly accurate results. Application of the derived correction reduces the off-axis distance related dose calculation error in wedged fields to < or =1%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".