Monte Carlo dose calculations for breast and lung permanent implant brachytherapy
Bibliographic record
Abstract
Current clinical practice for dose calculations for brachytherapy utilizes the TG-43 formalism where absorbed dose is calculated in a homogeneous water environment.The formalism does not account for the effect of tissue heterogeneities, interseed attenuation, and the finite dimensions of patients causing significant errors in calculated doses for low-energy permanent implant brachytherapy.As an alternative, Monte Carlo (MC) dose calculations model radiation transport and dose deposition in nonwater media but have only seen recent application to brachytherapy; issues relating to I would like to express my sincere gratitude towards my supervisors, Dr. Rowan Thomson and Dr. Dave Rogers.Together, they have provided me with immeasurable guidance, direction, and expertise.Dave sets a high standard of excellence that will surely affect me throughout my future endeavours.Additionally, his open-door policy and cheerful insights have always been greatly appreciated.Rowan's tireless involvement with the work of her students shows in her enthusiasm and willingness to always provide valuable guidance.The decision for Rowan to be my co-supervisor when she became a faculty member was easily one of the most advantageous events of my time at Carleton.I hope that she considers taking me on as her first student to be a fraction as positive a milestone as being her student has been for me.I would like to thank Dr. Keith Furutani of the Mayo Clinic for his highly important insights and perspective on our collaborative work.The majority of this thesis would not have been possible without him.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".