Influence of trace elements in human tissue in low-energy photon brachytherapy dosimetry
Bibliographic record
Abstract
The aim of this paper is to determine the dosimetric impact of trace elements in human tissues for low-energy photon sources used in brachytherapy. Monte Carlo dose calculations were used to investigate the dosimetric effect of trace elements present in normal or cancerous human tissues. The effect of individual traces (atomic number Z = 11-30) was studied in soft tissue irradiated by low-energy brachytherapy sources. Three other tissue types (prostate, adipose and mammary gland) were also simulated with varying trace concentrations to quantify the contribution of each trace to the dose distribution. The dose differences between cancerous and healthy prostate tissues were calculated in single- and multi-source geometries. The presence of traces in a tissue produces a difference in the dose distribution that is dependent on Z and the concentration of the trace. Low-Z traces (Na) have a negligible effect (<0.3%) in all tissues, while higher Z (K) had a larger effect (>3%). There is a potentially significant difference in the dose distribution between cancerous and healthy prostate tissues (4%) and even larger if compared to the trace-free composition (15%) in both single- and multi-sourced geometries. Trace elements have a non-negligible (up to 8% in prostate D(90)) effect on the dose in tissues irradiated with low-energy photon sources. This study underlines the need for further investigation into accurate determination of the trace composition of tissues associated with low-energy brachytherapy. Alternatively, trace elements could be incorporated as a source of uncertainty in dose calculations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".