SU‐E‐T‐420: Sensitivity of the Plan‐Class Specific Correction Factor to the Dose Distribution in Reference Dosimetry of Nonstandard Fields
Bibliographic record
Abstract
Purpose: To analyze the sensitivity of a plan‐class specific correction factor to dose distribution in nonstandard field dosimetry. Methods: Ten different TomoTherapy‐based IMRT fields were created on a water‐filled PMMA cylindrical phantom at the center of which the absorbed dose to water was measured. Dose distribution of each IMRT field was evaluated using three parameters: modulation factor (MF), modified conformal index (COIN) and homogeneity index (HI). The relative dose in each IMRT field was measured using a PTW micro liquid ion chamber. Based on the new dosimetry formalism, the plan‐class specific correction factor was measured for two 0.6 cm3 Farmer‐type chambers (Exradin A12 and NE2571) and a 0.06 cm3 Exradin A1SL chamber. We investigated the dependence of the measured correction factor on the three plan evaluation parameters. Results: Uncertainty on the measurement of the plan‐class specific correction factor was 0.2–0.4 % and 0.2–0.7 % for the Farmer‐type chambers and Exradin A1SL, respectively. When the dose homogeneity was better than 5 % along with values of MF and modified COIN smaller than 2.0 and 0.025, respectively, the correction factor of the IMRT field was not different from unity by more than 0.7 %. For more heterogeneous IMRT fields, the correction factor deviated from unity by up to 2.4 % for the Farmer‐type chambers because of increasing residual gradient effects. For the Exradin A1SL, while it was closer to unity, the correction factor was highly variable in different IMRT fields due to a more significant impact of positioning uncertainties on the response of the smaller chamber. Conclusions: We have shown that the plan‐class specific correction factor can be characterized as a function of plan evaluation parameters, especially for Farmer‐type chambers. This work provides a recipe based on quantifying dose distribution to accurately select air‐filled ionization chamber correction factors for nonstandard fields.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".