PO-0972: Clinical application and validation of a collapsed cone based algorithm for brachytherapy
Bibliographic record
Abstract
spectral photon fluence and of the fluence contributions by scattered and primary photons were evaluated.The effects of phantom material composition, especially of the organic polymer density and of the amount of inorganic additives, were also studied in terms of the resulting linear attenuation coefficient μ.Results: Significant differences were seen in the degree of water equivalence between the phantom materials covered by this study.While RW1, RW3, Solid Water, HE Solid Water, Virtual Water, Plastic Water DT and Plastic Water LR phantoms show dose deviations of less than 1.4% in all phantom sizes, Original Plastic Water (2015), Plastic Water (1995), Blue Water, polyethylene and polystyrene produce deviations up to 8.1 %.The role of PMMA is unique, showing deviations up to 4.3 % in phantoms with radii below 10 cm, but below 1 % in larger phantoms.Scattered photons with energies reaching down into the 25 keV region dominate the photon fluence at source distances exceeding 3.5 cm.The degree of water equivalence of a phantom material is correlated with the equivalence of its linear attenuation coefficient µ with that of water over a large energy range. Conclusion:The key feature of a suitable water substitute material is the agreement of its linear attenuation coefficient µ with that of water over a large range of photon energies.This precondition provides water equivalence with regard to the attenuation of the primary photons, the release of lowenergy photons by Compton scattering and their attenuation by a combination of the photoelectric and Compton effects.The instrument to achieve this goal is a balanced content of inorganic additives in a plastic phantom material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".