Poster - Thur Eve - 72: Improving Dose Homogeneity for TBI Using Irregular Surface Compensation Approach
Bibliographic record
Abstract
In a first step toward the implementation of irregular surface compensation for Total Body Irradiation (TBI), we have evaluated the ability of the Eclipse™ AAA to accurately predict dose distributions in water at extended SSD for a 6 MV beam (Hussain et al., 2010 JACMP, in press). As an extension of this work dose distribution calculations in a Rando® at extended SSD (185 cm) with and without the use of irregular surface compensation were performed. Calculations using surface compensation showed a significant improvement on dose uniformity across Rando® reducing dose heterogeneity on the lung region from ±10% to ±2%. In order to validate these dose calculations, TLD measurements were performed at the head thorax and abdominal regions of Rando®. An opposed beam pair (AP-PA) with a large field (56×43 cm2) was set to cover the head and neck down to the lower chest. The open and surface compensated beams were delivered at gantry zero with Rando® set at 185 cm SSD. 2200 MU and 400 MU were needed to deliver a 100 cGy dose to the midplane in Rando® for the compensated and open deliveries respectively. The agreement between measurements and calculations for the open beam was within ±3% except in the lung region where up to 4.9% dose overestimation was observed. For the irregular surface compensation delivery, an overall dose calculation/measurement agreement was within ±3%. These results indicate the potential clinical use of the irregular surface compensation for TBI at extended SSD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".