Evaluation of the analytical anisotropic algorithm in an extreme water–lung interface phantom using Monte Carlo dose calculations
Bibliographic record
Abstract
Our study compares the performance of the analytical anisotropic algorithm (AAA), a new superposition-convolution algorithm recently implemented in the Eclipse (Varian Medical Systems, Palo Alto, CA) Integrated Treatment Planning System (TPS), to that of the pencil beam convolution (PBC) algorithm in an extreme (C-shaped, horizontal and vertical boundaries) water-lung interface phantom. Monte Carlo (MC) calculated dose distributions for a variety of clinical beam configurations at nominal energies of 6-MV and 18-MV are used as benchmarks in the comparison. Dose profiles extracted at three depths (4, 10, and 16 cm), two-dimensional (2D) maps of the dose differences, and dose difference statistics are used to quantify the accuracy of both photon-dose calculation algorithms. Results show that the AAA is considerably more accurate than the PBC, with the standard deviation of the dose differences within a region encompassing the lung block reduced by a factor of 2 and more. Confidence limits with the AAA were 4% or less for all beam configurations investigated; with the PBC, confidence limits ranged from 3.5% to 11.2%. Finally, AAA calculations for the small 4 x 4 18-MV beam, which is poorly modeled by PBC (dose differences as high as 16.1%), provided the same accuracy as the PBC model of the 6-MV beams commonly acceptable in clinical situations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".