Dose homogeneity specification for reference dosimetry of nonstandard fields
Bibliographic record
Abstract
PURPOSE: To investigate the sensitivity of the plan-class specific correction factor to dose distributions in composite nonstandard field dosimetry. METHODS: A cylindrical water-filled PMMA phantom was constructed at the center of which reference absorbed dose could be measured. Ten different TomoTherapy(®)-based IMRT fields were created on the CT images of the phantom. The dose distribution for each IMRT field was estimated at the position of a radiation detector or ionization chamber. The dose in each IMRT field normalized to that in a reference 10 × 10 cm(2) field was measured using a PTW micro liquid ion chamber. Based on the new dosimetry formalism, a plan-class specific correction factor k(Q(pcsr),Q) (f(pcsr),f(ref)) for each field was measured for two Farmer-type chambers, Exradin A12 and NE2571, as well as for a smaller Exradin A1SL chamber. The dependence of the measured correction factor on parameters characterizing dose distribution was analyzed. RESULTS: Uncertainty on the plan-class specific correction factor measurement was in the range of 0.3%-0.5% and 0.3%-0.8% for the Farmer-type chambers and the Exradin A1SL, respectively. When the heterogeneity of the central region of the target volume was less than 5%, the correction factor did not differ from unity by more than 0.7% for the three air-filled ionization chambers. For more heterogeneous dose deliveries, the correction factor differed from unity by up to 2.4% for the Farmer-type chambers. For the Exradin A1SL, the correction factor was closer to unity due to the reduced effect of dose gradients, while it was highly variable in different IMRT fields because of a more significant impact of positioning uncertainties on the response of this chamber. CONCLUSIONS: The authors have shown that a plan-class specific correction factor can be specified as a function of plan evaluation parameters especially for Farmer-type chambers. This work provides a recipe based on quantifying dose distribution to accurately select air-filled ionization chamber correction factors for nonstandard fields.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".