Dose specification for<sup>192</sup>Ir high dose rate brachytherapy in terms of dose-to-water-in-medium and dose-to-medium-in-medium
Bibliographic record
Abstract
Dose calculation in high dose rate brachytherapy with (192)Ir is usually based on the TG-43U1 protocol where all media are considered to be water. Several dose calculation algorithms have been developed that are capable of handling heterogeneities with two possibilities to report dose: dose-to-medium-in-medium (Dm,m) and dose-to-water-in-medium (Dw,m). The relation between Dm,m and Dw,m for (192)Ir is the main goal of this study, in particular the dependence of Dw,m on the dose calculation approach using either large cavity theory (LCT) or small cavity theory (SCT). A head and neck case was selected due to the presence of media with a large range of atomic numbers relevant to tissues and mass densities such as air, soft tissues and bone interfaces. This case was simulated using a Monte Carlo (MC) code to score: Dm,m, Dw,m (LCT), mean photon energy and photon fluence. Dw,m (SCT) was derived from MC simulations using the ratio between the unrestricted collisional stopping power of the actual medium and water. Differences between Dm,m and Dw,m (SCT or LCT) can be negligible (<1%) for some tissues e.g. muscle and significant for other tissues with differences of up to 14% for bone. Using SCT or LCT approaches leads to differences between Dw,m (SCT) and Dw,m (LCT) up to 29% for bone and 36% for teeth. The mean photon energy distribution ranges from 222 keV up to 356 keV. However, results obtained using mean photon energies are not equivalent to the ones obtained using the full, local photon spectrum. This work concludes that it is essential that brachytherapy studies clearly report the dose quantity. It further shows that while differences between Dm,m and Dw,m (SCT) mainly depend on tissue type, differences between Dm,m and Dw,m (LCT) are, in addition, significantly dependent on the local photon energy fluence spectrum which varies with distance to implanted sources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".