Po‐Poster ‐ 22: Patient specific quality assurance for helical tomotherapy
Bibliographic record
Abstract
Helical tomotherapy is a highly integrated platform for delivering image guided and inverse planned IMRT. The TomoTherapy system combines highly conformal external beam IMRT with on board megavoltage CT, as well as integrated image fusion, moveable lasers, and a highly accurate couch. Patient specific QA, using film and ion chamber, helps ensure that the dose distributions that are delivered as planned. The patient specific QA employs several software tools, some provided by TomoTherapy Inc., and some developed in house. The QA process hinges on the integrated system ability to export patient delivery sinograms for calculation in a phantom. Analysis tools for comparing calculation and measured results are also present. Results from the film dosimetry system, ion chamber point measurements, as well as the use of the gamma function to assess the resulting measured vs. calculated distribution are presented. An in house computer code is employed which allows the gamma analysis to be constrained to region of interest. Results for ten recent of patients on in house research protocols are presented, showing that average point dose measurement are within 1.06% of the planning system. The gamma value from film is better for more recent patients; differences found in earlier patients are shown to result from the inherent difficulties in using film as a dosimeter (processing, need for care and refinement in calibration technique).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".