Validation of the Eclipse AAA algorithm at extended SSD
Bibliographic record
Abstract
The accuracy of dose calculations at extended SSD is of significant importance in the dosimetric planning of total body irradiation (TBI). In a first step toward the implementation of electronic, multi-leaf collimator compensation for dose inhomogeneities and surface contour in TBI, we have evaluated the ability of the Eclipse AAA to accurately predict dose distributions in water at extended SSD. For this purpose, we use the Eclipse AAA algorithm, commissioned with machine-specific beam data for a 6 MV photon beam, at standard SSD (100 cm). The model was then used for dose distribution calculations at extended SSD (179.5 cm). Two sets of measurements were acquired for a 6 MV beam (from a Varian linear accelerator) in a water tank at extended SSD: i) open beam for 5 x 5, 10 x 10, 20 x 20 and 40 x 40 cm2 field sizes (defined at 179.5 cm SSD), and ii) identical field sizes but with a 1.3 cm thick acrylic spoiler placed 10 cm above the water surface. Dose profiles were acquired at 5 cm, 10 cm and 20 cm depths. Dose distributions for the two setups were calculated using the AAA algorithm in Eclipse. Confidence limits for comparisons between measured and calculated absolute depth dose curves and normalized dose profiles were determined as suggested by Venselaar et al. The confidence limits were within 2% and 2 mm for both setups. Extended SSD calculations were also performed using Eclipse AAA, commissioned with Varian Golden beam data at standard SSD. No significant difference between the custom commissioned and Golden Eclipse AAA was observed. In conclusion, Eclipse AAA commissioned at standard SSD can be used to accurately predict dose distributions in water at extended SSD for 6 MV open beams.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".