SU‐E‐T‐07: Dose to Medium or Dose to a Water Cavity Embedded in Medium?
Bibliographic record
Abstract
Purpose: To investigate, by means of Monte Carlo (MC) simulations, the relationships of the dose to a local medium, Dm,m, and the dose to a water cavity embedded in the medium, Dw,m, the dose to a cell in the medium, Dc,m and the dose to a cell nucleus in the medium Dn,m for different energies used in brachytherapy and different cavity sizes. Methods: The Geant4 9.3 MC package was used to calculate Dm,m, Dw,m, Dc,m and Dn,m for a mono‐energetic photon beam impinging on one side of a cubic phantom. Concentric spherical cavities of 2–14 mum representing cells and their nuclei with four different chemical compositions were placed inside the phantom. Absorbed dose and electron fluence inside the cell and nucleus were scored. The dose to the cytoplasm and nuclei cavities was also estimated by means of large cavity theory (LCT) and small cavity theory (SCT) calculations. Results: Low brachytherapy energies are more sensitive to chemical composition of the cytoplasm and nucleus. Three of the simulated cell lines have water‐like behavior for adipose tissue while for breast and bone tissue the variations are larger. One cell line deviates for all the simulated tissues. Estimated results calculated with SCT deviates from MC calculated results for the low energies up to 50 keV for 2, 3 and 5 mum nucleus . For the 7 mum nucleus the difference is large up to 300 keV. The largest differences between LCT and MC results are observed for energies up to 100 keV. Conclusions: There is no universally applicable set of conversion factors used to convert Dm,m into the dose received by the cell nuclei. Water equivalent behavior varies depending on the chemical composition of the cell and nucleus and the surrounding tissue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".