Sci-Fri PM: Topics - 02: Evaluation of Dosimetric Variations in Partial Breast Seed Implant (PBSI) due to Patient Arm Position (Up vs. Down)
Bibliographic record
Abstract
The planning for PBSI is done with the patient's ipsilateral arm raised, however, anatomical changes and variations are unavoidable as the patient resumes her daily activities, potentially resulting in significant deviations in implant geometry from the treatment plan. This study aims to quantify the impact of the ipsilateral arm position on the geometry and dosimetry of the implant at eight weeks, evaluated on post-plans using the MIM Symphony™ software (MIM Software, Cleveland, OH). The average dose metrics for the three patients treated at the TBCC thus far using rigid fusion and contour transfer for the arms up position were 76% for the CTV V100, 61% for the PTV V100, and 37% for the PTV V200; and for the arms down position 81% for the CTV V100, 64% for the PTV V100, and 42% for the PTV V200. Qualitative analysis of the post-implant CT for one of the three patients showed poor agreement between the seroma contour transferred from the pre-implant CT and the seroma visible on the post-implant CT. To obtain a clinically accurate plan for that patient, contour modifications were used, yielding improved dose metric averages for the arms-up position for all three patients of 87% for the CTV V100, 68% for the PTV V100, and 39% for the PTV V200. Overall, the data available shows that dosimetric parameters increase with the patient's arm down, both in terms of coverage and in terms of the hot spot, and accrual of more patients may confirm this in a larger population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".