Technical Note: Experimental verification of<scp>EGS</scp>nrc calculated depth dose within a parallel magnetic field in a lung phantom
Bibliographic record
Abstract
Purpose The calculation of depth doses from a 6MVphoton beam in polystyrene usingEGSnrc Monte Carlo, within a parallel magnetic field, has been previously verified against measured data. The current work experimentally investigates the accuracy ofEGSnrc calculated depth doses in lung within the same parallel magnetic field. Methods Two cylindrical bore electromagnets produced a magnetic field parallel to the central axis of a Varian Silhouette beam. A Gammex lung phantom was used, along with a parallel plate ion chamber, for the depth dose measurements. Two experimental setups were investigated: top of phantom coinciding with the top of the magnet's bore, and top of phantom coinciding with the center of the bore.EGSnrc was modified to read the 3D magnetic field distribution and then used to simulate the depth dose in lung. Results The parallel magnetic field caused measurable increases in dose at the surface and in the buildup region for both setups. For the setup where the top of the lung phantom coincides with the top of the magnet, the surface dose increased by ~11% compared to the no magnetic field case but the depth of maximum dose remained unchanged. When the phantom's top surface coincided with the center of the magnet, the surface dose increased by 32% and dose maximum occurred at a shallower depth.EGSnrc was able to calculate these dose increases due to the magnetic field accurately for both setups. All the simulated depth dose values were within 2% (with respect to Dmax) of the measured ones, and most of the investigated points were within 1.5%. Conclusions Surface and dose increases due to a parallel magnetic field have been measured in a lung phantom at two separate locations within the magnetic field.EGSnrc has been shown to match these measurements to within 2%.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".