Seventieth Annual Scientific Meeting of Canadian Organization of Medical Physicists, Delta Hotels, Regina, Saskatchewan, June 5–8, 2024
Bibliographic record
Abstract
Purpose: Clinically employed techniques to monitor movements in a deep inspiration breath hold breast treatment are limited to monitoring the patient's surface.The delivered dose variation in target and OARs due to complex motion from variation in depth of breath hold is not fully understood.Methods: A cohort of 50 patients with left-sided breast treatment plans were considered for this study.CT images of these patients at breath hold (BH) and free breath (FB) positions are used to establish a deformation vector field (DVF) between these two postures using Velocity software by Varian Medical Systems.To account for the correct anatomical location of the tissues at deviations from the BH target, a scaling factor is established based on the difference between the average DIBH and FB period based on the RPM/RGSC breathing traces acquired during CT simulation.CT images for BH deviations from +5 to -5 mm are then generated.Varian's Eclipse software is employed to calculate the dose at the target and organs at risk at each deviation.Results: The PTV D95 and Lung mean doses are sensitive to the deviation from the breath hold target, with PTV more sensitive to undershoot.In contrast, the mean lung dose is sensitive to both overshoot and undershoot.In general, the delivered dosimetry deviates from the planned dose more for VMAT than 3D CRT when BH target is not perfectly achieved. Conclusion:In a DIBH treatment, 3D CRT techniques is dosimetrically advantageous and more forgiving to tolerance over VMAT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.038 |
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".