Mathematical solutions of the TG-43 geometry function for curved line, ring, disk, sphere, dome and annulus sources, and applications for quality assurance
Bibliographic record
Abstract
Analytic solutions for the TG-43 geometry function for curved line, ring, disk, sphere, dome and annulus shapes containing uniform distributions of air-kerma are derived. These geometry functions describe how dose distributions vary strictly due to source geometry and not including attenuation or scatter effects. This work extends the use of geometry functions for individual sources to applicators containing multiple sources. Such geometry functions may be used to verify dose distributions computed using advanced techniques, including QA of model-based dose calculation algorithms. The impact of source curvature on linear and planar implants is considered along with the specific clinical case of brachytherapy eye plaques. For eye plaques, the geometry function for a domed distribution is used with published Monte Carlo dose distributions to determine a radial dose function and anisotropy function which includes all the scatter and attenuation effects due to the phantom, eye plaque and sources. This TG-43 model of brachytherapy eye plaques exactly reproduces azimuthally averaged Monte Carlo calculations, both inside and outside the eye.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".