Application de la scintillation liquide pour caractériser une source de curiethérapie par émetteurs-alphas diffusant
Bibliographic record
Abstract
Cancer is the leading cause of death in Canada. Many cancer treatments are using chemotherapy, surgery and radiotherapy. In radiotherapy, photons are the most used ionizing radiation, however alpha particles have higher radiobiological impact which increases the efficiency of patient treatment delivery. Alpha Tau Medical Ltd. (Tel Aviv, Israël) has developed a new brachytherapy method using radioactive seeds. The seeds called DaRT, for Diffusing alpha emitters Radiation Therapy, are composed of 224-Ra atoms which come from 228-Th generator. Currently, these seeds are characterized by an alpha-spectrometer and Geiger-Muller counter or well chamber for quality control. This project offers a new characterization of DaRT seeds using liquid scintillation. Liquid scintillation allows alpha and beta particles detection with the help of liquid cocktail and employing a scintillation counter. The characterization with liquid scintillation allows establishing and quantifying 228-Th trace contamination on the DaRT seeds. Also, it provides a method for seed quality control before they are used on patients by estimating their activity from spectrums established with the liquid scintillation counter. The spectrums obtained also give the possibility of dose estimation using either mass or mass stopping power in water. The results of the dose are compared to expected values from the literature and to simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".