Monte Carlo investigation of dose distribution of uniformly and non‐uniformly loaded standard and notched eye plaques
Bibliographic record
Abstract
To investigate the effect of using non-uniform loading and notched plaques on dose distribution for eye plaques. Using EGSnrc Monte Carlo (MC) simulations, we investigate eye plaque dose distributions in water and in an anatomically representative eye phantom. Simulations were performed in accordance with TG43 formalism and compared against full MC simulations which account for inter-seed and inhomogeneity effects. For standard plaque configurations, uniformly and non-uniformly loaded plaque dose distributions in water showed virtually no difference between each other. For standard plaque, the MC calculated dose distribution in planes parallel to the plaque is narrower than the TG43 calculation due to attenuation at the periphery of the plaque by the modulay. MC calculated the dose behind the plaque is fully attenuated. Similar results were found for the notched plaque, with asymmetric attenuation along the plane of the notch. Cumulative dose volume histograms showed significant reductions in the calculated MC doses for both tumor and eye structures, compared to TG43 calculations. The effect was most pronounced for the notch plaque where the MC dose to the optic nerve was greatly attenuated by the modulay surrounding the optic nerve compared to the TG43. Thus, a reduction of optic nerve D95% from 14 to 0.2 Gy was observed, when comparing the TG43 calculation to the MC result. The tumor D95% reduced from 89.2 to 79.95 Gy for TG43 and MC calculations, respectively. TG43 calculations overestimate the absolute dose and the lateral dose distribution of both standard and notched eye plaques, leading to the dose overestimation for the target and organs at risk. The dose matching along the central axis for the non-uniformly loaded plaques to that of uniformly loaded ones was found to be sufficient for providing comparable coverage and can be clinically used in eye-cancer-busy centers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".