Poster - Thurs Eve-32: Dose errors related to the treatment couch
Bibliographic record
Abstract
Modern radiotherapy linacs often use carbon fibre for their couch tops due to its radio translucent properties. Beam attenuation by the couches is often ignored during planning and MU calculation. This work examines beam attenuation and loss of “skin sparing” (dose build up region) when various photon beams transit either the MedTec (Siemens) or Medical Intelligence (Elekta) couches. Additionally, measured doses were compared to CMS treatment planning system (XiO version 4.33.02) predictions. We found the two couches to have different structures, resulting in different attenuation signatures as a function of gantry angle. For normal beam incidence the Siemens and Elekta couches had radiological thicknesses of 4.5 mm and 6.0 mm, respectively. For a normal incidence 10×10 cm2 6MV beam the surface dose after couch transmission was 93% vs. 83% for Elekta and Siemens, respectively. Conversely, the increased mass on the lateral edge of the Siemens couch resulted in a maximum attenuation (6 MV 5×5 cm2 beams) of 8% compared to 5% by the Elekta couch. Incorporating the treatment couch as part of the patient planning CT allowed the CMS TPS model to calculate couch attenuation within 1% of measurement, except at the very edge of the Siemens couch, where the attenuation is strongly gantry angle dependent. The CMS beam model was also able to predict the loss of skin sparing within 1%. In conclusion, the two patient couches are different, but both can significantly affect patient dose which can be accounted for in the CMS TPS.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".