A Monte Carlo derived TG‐51 equivalent calibration for helical tomotherapy
Bibliographic record
Abstract
Helical tomotherapy (HT) requires a method of accurately determining the absorbed dose under reference conditions. In the AAPM's TG-51 external beam dosimetry protocol, the quality conversion factor, kQ, is presented as a function of the photon component of the percentage depth-dose at 10 cm depth, %dd(10)x, measured under the reference conditions of a 10 x 10 cm2 field size and a source-to-surface distance (SSD) of 100 cm. The value of %dd(10)x from HT cannot be used for the determination of kQ because the design of the HT does not meet the following TG-51 reference conditions: (i) the field size and the practical SSD required by TG-51 are not obtainable and (ii) the absence of the flattening filter changes the beam quality thus affecting some components of kQ. The stopping power ratio is not affected because of its direct relationship to %dd(10)x. We derive a relationship for the Exradin A1SL ion chamber converting the %dd(10)x measured under HT "reference conditions" of SSD=85 cm and a 5 x 10 cm2 field-size [%dd(10)x[HT Ref]], to the dosimetric equivalent value under for TG-51 reference conditions [%dd(10)x[HT TG-51]] for HT. This allows the determination of kQ under the HT reference conditions. The conversion results in changes of 0.1% in the value of kQ for our particular unit. The conversion relationship should also apply to other ion chambers with possible errors on the order of 0.1%.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".