Sci—Fri PM: Delivery — 07: Cyberknife relative output factor measurements using fiber‐coupled luminescence, MOSFETS and RADPOS dosimetry system
Bibliographic record
Abstract
Novel dosimetry systems based on Al2O3:C radioluminescence (RL) and a 4D dosimetry system (RADPOS) from Best Medical Canada were used to measure the relative output factor (ROF) on Cyberknife. Measurements were performed in a solid water phantom at the depth of 1.5 cm and SSD = 78.5 cm for cones from 5 to 60 mm. ROFs were also measured using a mobileMOSFET system (Best Medical Canada) and EBT1 and EBT2 GAFCHROMIC® (ISP, Ashland) radiochromic films. For cone sizes 12.5–60 mm all detector results were in agreement within the measurement uncertainty. The microMOSFET/RADPOS measurements (published corrections applied) yielded ROFs of 0.650 ± 1.9%, 0.811 ± 0.9% and 0.843 ± 1.7% for the 5, 7.5 and 10 mm cones, respectively, and were in excellent agreement with radiochromic film values (averaged for EBT1 and EBT2) of 0.645 ± 1.4%, 0.806 ± 1.1% and 0.859 ± 1.1%. Monte‐Carlo calculated correction factors were applied to the RL readings to correct for excessive scatter due to the relatively high effective atomic number of Al2O3 (Z=10.2) compared to water for the 5, 7.5 and 10 mm cones. When these corrections are applied to our RL detector measurements, we obtain ROFs of 0.656 ± 0.3% and 0.815 ± 0.3% and 0.865 ± 0.3% for 5, 7.5 and 10 mm cones. Our study shows that the microMOSFET/RADPOS and optical fiber‐coupled RL dosimetry system are well suited for Cyberknife cone output factors measurements over the entire range of field sizes, provided that appropriate correction factors are applied for the smallest cone sizes (5, 7.5 and 10 mm).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.174 | 0.052 |
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".