Dose to water versus dose to medium from cavity theory applied to small animal irradiation with kilovolt x-rays
Bibliographic record
Abstract
Dose reporting is a matter of concern in the preclinical field as the different dose descriptors dose-to-water-in-medium [Formula: see text] and dose-to-medium-in-medium [Formula: see text] coexist. For kV photons differences between both quantities are expected to be amplified due to photon energy absorption coefficients differences for different media, and could represent a limiting factor for accurate translation of pre-clinical research into clinical trials. The main goal of this study was to analyse the relationship between [Formula: see text] and [Formula: see text] for kV irradiation of small animals, using different flavours of the intermediate cavity theory (ICT). Irradiations of mathematical phantoms and a mouse CT scan, both with different voxel sizes and materials, were investigated. A modified version of the Monte Carlo code DOSXYZnrc was used to derive [Formula: see text] and convert to [Formula: see text] using ICT. Local photon spectra were generated in different regions of the mouse. Depending on energy and cavity size, which we equate to the voxel size, [Formula: see text] ranged from 0.68 to 4.37 times [Formula: see text]. Higher kV energy combined with very small cavity sizes yielded decreased [Formula: see text] in comparison to [Formula: see text]; this behaviour was reversed for larger cavities combined with lower kV energies. Hence, the impact of the cavity dimensions on estimated [Formula: see text] is significant on pre-clinical kV beams. [Formula: see text] and [Formula: see text] in the ex vivo male mouse were found to differ by -29% to 286%. Caution is advised when using the ICT due to a lack of consensus on weighting factor (d-parameter) deriving methods; for the same irradiation conditions, different d-values affected [Formula: see text] up to 20%. Pre-clinically, such divergence between dose descriptors could enable biological damage. The abiding debate over which quantity to favour is foreseen to linger while it is unclear which quantity correlates better with the biological effects of ionizing irradiation: preclinical radiotherapy might represent an ideal platform for measurement-based studies to settle this fundamental question. Finally, dose distribution comparisons require caution and should use the same reporting quantity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".