Monte Carlo calculated beam quality correction factors for two cylindrical ionization chambers in photon beams
Bibliographic record
Abstract
Purpose Although several studies provide data for reference dosimetry, the SNC600c and SNC125c ionization chambers (Sun Nuclear Corporation, Melbourne, FL) are in clinical use worldwide for which no beam quality correction factors k Q are available. The goal of this study was to calculate beam quality correction factors k Q for these ionization chambers according to dosimetry protocols TG-51, TRS 398 and DIN 6800-2. Methods Monte Carlo simulations using EGSnrc have been performed to calculate the absorbed dose to water and the dose to air within the active volume of ionization chamber models. Both spectra and simulations of beam transport through linear accelerator head models were used as radiation sources for the Monte Carlo calculations. Results k Q values as a function of the respective beam quality specifier Q were fitted against recommended equations for photon beam dosimetry in the range of 4 MV to 25 MV. The fitting curves through the calculated values showed a root mean square deviation between 0.0010 and 0.0017. Conclusions The investigated ionization chamber models (SNC600c, SNC125c) are not included in above mentioned dosimetry protocols, but are in clinical use worldwide. This study covered this knowledge gap and compared the calculated results with published k Q values for similar ionization chambers. Agreements with published data were observed in the 95% confidence interval, confirming the use of data for similar ionization chambers, when there are no k Q values available for a given ionization chamber.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".