Update of the CLRP eye plaque brachytherapy database for photon-emitting sources
Bibliographic record
Abstract
Purpose: To update and extend the Carleton Laboratory for Radiotherapy Physics (CLRP) Eye Plaque (EP) dosimetry database for low-energy photon-emitting brachytherapy sources using egs brachy, an open-source EGSnrc application.The previous database, CLRP EPv1, contained datasets for the Collaborative Ocular Melanoma Study (COMS) plaques (10-22 mm diameter) with 103 Pd or 125 I seeds (BrachyDose-computed, 2008).The new database, CLRP EPv2, consists of newlycalculated 3D dose distributions for 17 plaques [8 COMS, 5 Eckert & Ziegler BEBIG, and 4 others representative of models used worldwide] for 103 Pd, 125 I, and 131 Cs seeds.Acquisition and Validation Methods: Plaque models are developed with egs brachy, based on published/manufacturer dimensions and material data.The BEBIG plaques (modelled for the first time) are identical in dimensions to COMS plaques but differ in elemental composition and/or density.Previously-benchmarked seed models are used.Eye plaques and seeds are simulated at the centre of fullscatter water phantoms, scoring in (0.05 cm) 3 voxels spanning the eye for scenarios: (i) 'HOMO': simulated TG43 conditions; (ii) 'HETERO': eye plaques and seeds fully modelled; (iii) 'HETsi' (BEBIG only): one seed is active at a time with other seed geometries present but not emitting photons (inactive); summation over all i seeds in a plaque then yields 'HETsum' (includes interseed effects).For validation, doses are compared to those from CLRP EPv1 and published data.Data Format and Access:
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".