Poster - Thurs Eve-20: Analysis of dosimetric differences between dose-to-water vs. dose-to-medium calculations for electron beams
Bibliographic record
Abstract
Purpose: With the advent of commercial Monte Carlo based treatment planning systems (TPS) typically calculating dose-to-medium, Dm, as opposed to dose-to-water, Dw, calculated by conventional TPS, a thorough analysis of differences between these treatments plans is required. Such an analysis has not yet been carried out. The purpose of our study was to evaluate dosimetric differences between such plans generated with a commercial MC based TPS. Materials and Methods: The analysis included plans of 53 breast cancer patients treated with electron beams ranging from 6–20 MeV. These plans were originally calculated using the Dm approach. Keeping the original beam arrangements and the same calculation parameters, the plans were recalculated using the Dw approach. The comparison between Dm and Dw plans was performed by means of dose volume histograms and isodose distributions on the corresponding CT slices. Results and Conclusions: The plans calculated using Dm vs. Dw show some differences, with magnitudes depending on the location of the tumor and organs at risk and the beam energy. The largest difference was found for the treatment of the chest wall after complete mastectomy with 13 MeV beam. The dose to internal mammary nodes was 43.6 Gy and 39.0 Gy for Dm and Dw approach, respectively. This amounts to 8.1% difference in the maximum dose delivered to that volume. For the same patient, the dose received by the right lung was 13.5 and 15.4 Gy for Dm and Dw approach, respectively, which amounts to 3.6% difference in dose delivered to this lung.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".