THE IMPLEMENTATION OF STANDARDIZED BREAST AND REGIONAL NODAL VMAT IN AN ACADEMIC CENTRE
Bibliographic record
Abstract
Breast VMAT is a complicated technical treatment that can be used for complex volumes and patient anatomy challenges. The standardized implementation of these treatments is not well documented or described. Our centre has created a robust workflow and education program which incorporates standardized planning templates, robust planning, and simple yet accurate IGRT workflow, to reduce the burden of advanced IGRT but maintain treatment accuracy with mobile targets. We retrospectively compared acquired IGRT before the implementation of departmental IGRT standards versus afterwards. 5 patients from 3 cohorts of locoregional breast cancer treatments were identified: before regular use of CBCT, after CBCT was in regular use and before IGRT guidelines were implemented, and after CBCT and IGRT guideline implementation. Patients were represented from both free breathing and deep inspiratory breath hold treatments. Time from setup to beam on, number of kV, MV, and CBCT images were recorded from day 1 and day 5 of treatment. The average time in minutes to start the first treatment for the 3 cohorts were 13.5, 26.3, and 17.4, respectively. The average amount of both kV and MV images acquired for day 1 were 4.6, 3.8 and 2.2. The average number of CBCTs acquired day 1 were 0.2, 2 and 1.4 for the cohorts. These differences were maintained and comparable for day 5 setups. The use of departmental imaging standards reduced the amount of CBCTs acquired on day 1 for patients, and resulted in an average of one third reduction in time to beam on for patient starts without sacrificing setup accuracy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".