Investigation of systematic uncertainties in Monte Carlo-calculated beam quality correction factors
Bibliographic record
Abstract
Modern Monte Carlo codes allow for the calculation of ion chamber specific beam quality correction factors k(Q), which are needed for dosimetry in radiotherapy. While statistical (type A) uncertainties of the calculated data can be minimized sufficiently, the influence of systematic (type B) uncertainties is mostly unknown. This study presents an investigation of systematic uncertainties of Monte Carlo-based k(Q) values for a NE2571 thimble ion chamber, calculated with the EGSnrc system. Starting with some general investigation on transport parameter settings, the influence of geometry and source variations is studied. Furthermore, a systematic examination of uncertainties due to cross section is introduced by determining the sensitivity of k(Q) results to changes in cross section data. For this purpose, single components of the photon cross sections and the mean excitation energy I in the electron stopping powers are varied. The corresponding sensitivities are subsequently applied with information of standard uncertainties for the cross section data found in the literature. It turns out that the calculation of k(Q) factors with EGSnrc is mostly insensitive to transport settings within the statistical uncertainties of approximately 0.1%. Severe changes in the dimensions of the chamber lead to comparatively small, insignificant changes. Further, the inclusion of realistic beam models, delivering a complete phase space instead of simple photon spectra, does not significantly influence the result. However, the uncertainties in electron cross sections have an impact on the final uncertainty of k(Q) to a comparatively large degree. For the NE2571 chamber investigated in this work, this uncertainty amounts to 0.4% at 24 MV, decreasing to 0.2% at 6 MV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".