Bibliographic record
Abstract
Gamma emitting radioisotopes such as 75Se, 169Yb and 153Gd are attractive candidates as brachytherapy radiation sources. The first aim of this work was to evaluate the potential of these novel sources, in terms of radiobiological advantages, for applications in high dose rate (HDR) brachytherapy. The radiation quality of these potential sources was evaluated using a combination of track structure simulations and numerical microdosimetric techniques, and the weighting factor related to fractionated radiotherapy was predicted from the microdosimetric distributions. The weighting factors were 1.10, 1.14, and 1.19 for 75Se, 169Yb and 153Gd, respectively, which are considerably above unity. The second aim of this work was to examine the viability of production of 153Gd sources through radiochemistry experiments performed at McMaster Nuclear Reactor (MNR). A new, lower, effective thermal neutron capture cross section was evaluated, and the maximum achievable specific activity was predicted to be about 70 Ci/g of 152Gd. In addition, a purification method and a method to load the radioisotope onto a substrate for encapsulation were demonstrated to be effective. Finally, brachytherapy sources with intermediate energy have radial dose functions that are ideal for HDR brachytherapy application and present various dosimetric, microdosimetric, and radiobiological advantages over sources currently used in HDR brachytherapy, while reducing shielding requirements for the brachytherapy suite. Such sources can also potentially be used in combination with a rotating shield delivery system to deliver intensity modulated brachytherapy (IMBT).
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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 | 0.001 |
| 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 | 0.000 | 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".