Poster ‐ 42: TB ‐ ARC: A Total Body photon ARC technique using a commercially available linac
Bibliographic record
Abstract
We have developed a total body photon irradiation technique using multiple overlapping open field arcs (TB‐ARC). This simple technique uses predetermined arc‐weights, with MUs calculated as a function of prescription depth only. Patients lie on a stretcher, in the prone/supine treatment position with AP/PA arcs. This treatment position has many advantages including ease of delivery (especially for tall, pediatric or compromised patients), dose uniformity, simplicity for organ shielding, and imaging capabilities. Using a Varian TrueBeam linac, 14 arcs using 40×40 cm2 6 MV open photon beams, sweeping across 10 degrees each, complete a 140 degree arc. The nominal SSD at zero degrees is 200 cm. Arcs at the sweep limits (+/− 70 degrees) are differentially weighted and deliver a dose within 10% of the prescription on central axis, at a depth of 10 cm over a superior‐inferior length of 275 cm. CT planning using Varian Eclipse enables dose evaluation. A custom made beam spoiler, consisting of a 2.5 m sheet of polycarbonate (6 mm thick) increases the surface dose from 45% to 90%. This beam spoiler also serves as a support in the event that differential attenuation is required for organs such as lung, heart, liver, kidneys. The geometry of the sweeping beam technique limits organ dose (using varying thicknesses of melting alloy) to about 20% and 40% of prescription at dmax and midplane respectively. Digital imaging with a portable DR cassette enables proper attenuator location prior to treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".