Poster - 07: Investigations of the Advanced Collapsed-cone Engine for HDR Brachytherapy Scalp Treatments
Bibliographic record
Abstract
Purpose: To present an investigation of the Advanced Collapsed-cone Engine (ACE) in Oncentraê Brachy (OcB) v4.5 using a tissue equivalent phantom modeling scalp brachytherapy (BT) treatments. Methods: A slab phantom modeling the skin, skull, brain and mold was used. A dose of 400cGy was prescribed to just above the skull layer using TG-43 and was delivered using an HDR afterloader. Measurements were made using Gafchromic™ EBT3 film at four depths within the phantom. The TG-43 planned and film measured doses were compared to the standard (sACE) and high (hACE) accuracy ACE options in OcB between the surface and below the skull. Results: The average difference between the TG-43 calculated and film measured doses was −11.25±3.38% when there was no air gap between the mold and skin; sACE and hACE doses were on average lower than TG-43 calculated doses by 3.41±0.03% and 2.45±0.03%, respectively. With a 3mm air gap between the mold and skin, the difference between the TG-43 calculated and measured doses was −8.28±5.76%; sACE and hACE calculations yielded average doses 1.87±0.03% and 1.78±0.04% greater than TG-43, respectively. Conclusions: TG-43, sACE, and hACE were found to overestimate doses below the skull layer compared to film. With a 3mm air gap between the mold and skin, sACE and hACE more accurately predicted the film dose to the skin surface than TG-43. More clinical variations and their implications are currently being investigated.
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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".