Sci-Fri AM: Mountain - 01: Validation of a new formulism and the related correction factors on output factor determination for small photon fields
Bibliographic record
Abstract
Small field dosimetry measurements including output factors are difficult due to lack of charged-particle equilibrium, occlusion of the radiation source, the finite size of detectors, and non-water equivalence of detector components. With available detectors significant variations could be measured that will lead to incorrect delivered dose to patients. IAEA/AAPM have provided a framework and formulation to correct the detector response in small photon fields. Monte Carlo derived correction factors for some commonly used small field detectors are now available, however validation has not been performed prior to this study. An Exradin A16 chamber, EDGE detector and SFD detector were used to perform the output factor measurement for a series of conical fields (5–30mm) on a Varian iX linear accelerator. Discrepancies up to 20%, 10% and 6% were observed for 5, 7.5 and 10 mm cones between the initial output factors measured by the EDGE detector and the A16 ion chamber, while the discrepancies for the conical fields larger than 10 mm were less than 4%. After the application of the correction, the output factors agree well with each other to within 1%. Caution is needed when determining the output factors for small photon fields, especially for fields 10 mm in diameter or smaller. More than one type of detector should be used, each with proper corrections applied to the measurement results. It is concluded that with the application of correction factors to appropriately chosen detectors, output can be measured accurately for small 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".