Sci‐Thur PM – Colourful Interactions: Highlights 01: Design to delivery of spatially fractionated mini‐beam canine radiotherapy
Bibliographic record
Abstract
Spatial fractionation of radiation using arrays of narrow parallel micro‐planar beams (less than 1 mm), is a relatively new concept with many unknowns specifically within the underlying biology of cell death. A tungsten collimator has been designed to produce mini‐beams with a Varian linear accelerator for translational animal research into the effectiveness of spatial fractionation mini‐beam radiotherapy (MBRT). This work presents the treatment planning process and workflow for the application of MBRT treatments within a clinical study. For patient dose calculations, the MBRT collimator was incorporated into a Monte Carlo based treatment planning system called MMCTP. Treatment planning was split between Eclipse and MMCTP, as the field apertures were determined within Eclipse prior to being sent to MMCTP for dose calculations. The calculated plan was transferred back into Aria with updated MUs per field for patient treatment. Patients were positioned within a vac‐lock bag lying prone with a bite block and a thermoplastic mask to immobilize the head. Prior to treatment, a delivery verification plan was created within MMCTP. DQA output measurements of the treatment fields agreed with the calculated dose to within 1.5%. We have presented a workflow for MBRT treatments that include the planning technique, dose calculation method, DQA process and data integration into a record and verify system. The clinical study following this workflow represent the first series of linac based MBRT patients and depending on the clinical outcome of the study, our technique could be applied to human MBRT treatments.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".