Monte Carlo investigation of single cell beta dosimetry for intraperitoneal radionuclide therapy
Bibliographic record
Abstract
Single event spectra for five beta-emitting radionuclides (Lu-177, Cu-67, Re-186, Re-188, Y-90) were calculated for single cells from two source geometries. The first was a surface-bound isotropically emitting point source and the second was a bath of free radioactivity in which the cell was submerged. Together these represent a targeted intraperitoneal radionuclide therapy. Monoenergetic single event spectra were calculated over an energy range of 11 keV to 2500 keV using the EGSnrc Monte Carlo system. Radionuclide single event spectra were constructed by weighting monoenergetic single event spectra according to radionuclide spectra appropriate for each source geometry. In the case of surface-bound radioactivity, these were radionuclide beta decay spectra. For the free radioactivity, a continuous slowing down approximation spectrum was used that was calculated based on the radionuclide decay spectra. The frequency mean specific energy per event increased as the energy of the beta emitter decreased. This is because, at these energies, the stopping power of the electrons decreases with increasing energy. The free radioactivity produced a higher frequency mean specific energy per event than the corresponding surface-bound value. This was primarily due to the longer mean path length through the target for this geometry. This information differentiates the radionuclides in terms of the physical process of energy deposition and could be of use in the radionuclide selection procedure for this type of therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".