Po-Poster - 05: An evaluation of treatment dose error due to beam attenuation from a carbon fiber table top
Bibliographic record
Abstract
The emergence of carbon fiber materials for use in radiation therapy was largely due to its high mechanical strength, low specific density, and its perceived radio-translucence. These characteristics made it an ideal material for the patient support assembly utilized during treatments. Modern radiation therapy commonly employs beams delivered at oblique angles. With the introduction of carbon fiber table tops the attenuation of the couch is often ignored during treatment planning and there is little effort to avoid intersection of the beam with the table during patient setup. The perception that carbon fiber is relatively radio-translucent has permitted it to be used while neglecting to consider the effects it may have on the dose to the patient. In this study we have measured the attenuation of the couch under various conditions for 6 and 18 MV photons. We have found dose reductions in phantom of greater than 7%. We further investigate the ability of a commercial treatment planning system (Theraplan Plus) to properly model this effect during the planning stage. Our results show that incorporating the carbon fiber couch in the patient model reduces the dose error to less than 2%. These results reveal that it is worthwhile addressing this real clinical problem in such a manner that it can be routinely considered for all patient treatments. Thus, practical suggestions are proposed for the incorporation of the treatment tabletop into patient treatment planning dose calculations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.006 | 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".