SU‐E‐T‐424: Improved Dosimetric Accuracy for Cyberknife Patient Plans Using a Dual‐Detector Measurement Method for Relative Output Factors
Bibliographic record
Abstract
Purpose: The measurement of output factors (OFs) for small fields can lead to large dosimetric errors if detector effects are not accounted for. With its high spatial resolution and tissue equivalence, GAFCHROMIC film provides a correction free measure of OFs. We recently changed the OFs used in our Cyberknife planning system from uncorrected diode values to a dual detector method employing a diode with Monte‐Carlo corrections for the smallest collimators and a micro ion chamber for collimators >10 mm in diameter. Methods: We measured OFs for the CyberKnife G4 fixed collimators (5 to 60 mm) using an A16 microchamber and an Edge diode detector. The diode measured OFs for collimator sizes <10 mm were corrected using Monte‐Carlo correction factors. OFs were also measured using GAFCHROMIC film. We evaluated how this change in OFs influenced the dosimetric accuracy of patient specific QA measurements for 13 patient plans (9 before and 4 after the OF change) using film and the A16 chamber. Results: The OFs measured using the dual‐detector method agree with film to within two standard deviations for the full range of collimator sizes. When the dual detector method OFs are used, we achieve better dosimetric agreement (<2 sigma for pixels within the 80% isodose) than with uncorrected diode OFs for all patient specific QA plans measured using film. For patient specific QA using the microchamber, we get good agreement (<3%) for collimator sizes >5 mm, with differences observed for the 5 mm collimator consistent with volume averaging and a 1 mm setup uncertainty. Conclusions: OFs can be determined consistently using the dual‐detector method and verified using film. For patient specific QA, we achieve good agreement with microchambers for collimators >5 mm in diameter but film is the most appropriate detector for patient specific QA using the 5 mm collimator.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".