Does a Three-Field Electron/Minitangent Photon Technique Offer Dosimetric Advantages to a Multifield, Photon-Only Technique for Accelerated Partial Breast Irradiation?
Bibliographic record
Abstract
PURPOSE: To determine whether 3-field electron/minitangent photons (mixed-modality) technique improved dosimetry compared with a multifield, photon-only technique for accelerated partial breast irradiation (APBI). MATERIALS AND METHODS: Subjects were 20 breast cancer patients previously treated with photon-only APBI as part of a clinical trial to a dose of 38.5Gy/10 fractions in 5 days. Mixed-modality replans were compared with the photon-only plans with regards to planning target volume (PTV) coverage, conformity, homogeneity, and doses to normal tissues. RESULTS: The mixed-modality plans had similar PTV coverage, with more conformality, and reduced the volume of the ipsilateral breast receiving > or =95% and > or =50% doses, but had the pitfalls of less homogeneity and increased exposure of the ipsilateral lung and heart receiving low-dose radiation because of the exit dose of the electron beam. Factors associated with this increase were medial/deep-seated tumor beds, large tumor beds with PTV/Ipsilateral Breast ratio >20%, and use of high energy electrons. CONCLUSIONS: The three-field electron/minitangent photon APBI technique was more conformal and reduced the dose to the ipsilateral breast but had the disadvantage of exposing increased volumes of heart and ipsilateral lung to low-dose radiation compared with the photon-only technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.000 |
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".