Performance characteristics of an independent dose verification program for helical tomotherapy
Bibliographic record
Abstract
<br>Helical tomotherapy with its advanced method of intensity-modulated radiation therapy delivery has been used clinically for over 20 years. The standard delivery quality assurance procedure to measure the accuracy of delivered radiation dose from each treatment plan to a phantom is time-consuming. RadCalc<sup>®</sup>, a radiotherapy dose verification software, has released specifically for beta testing a module for tomotherapy plan dose calculations. RadCalc<sup>®</sup>'s accuracy for tomotherapy dose calculations was evaluated through examination of point doses in ten lung and ten prostate clinical plans. Doses calculated by the TomoHDA™ tomotherapy treatment planning system were used as the baseline. For lung cases, RadCalc<sup>®</sup> overestimated point doses in the lung by an average of 13%. Doses within the spinal cord and esophagus were overestimated by 10%. Prostate plans showed better agreement, with overestimations of 6% in the prostate, bladder, and rectum. The systematic overestimation likely resulted from limitations of the pencil beam dose calculation algorithm implemented by RadCalc<sup>®</sup>. Limitations were more severe in areas of greater inhomogeneity and less prominent in regions of homogeneity with densities closer to 1 g/cm<sup>3</sup>. Recommendations for RadCalc<sup>®</sup> dose calculation algorithms and anatomical representation were provided based on the results of the study.<br>
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".