Boron neutron capture therapy in the new age of accelerator-based neutron production and preliminary progress in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Each year more than 3000 Canadians are diagnosed with brain cancers like glioblastoma multiforme or recurrent head and neck cancers, which are difficult to treat with conventional radiotherapy techniques. One of the most clinically promising treatments for these cancers is boron neutron capture therapy (BNCT). This procedure involves selectively introducing a boron delivery agent into tumor cells and irradiating them with a neutron beam, which kills the cancer cells due to the high-linear energy transfer radiation produced by the 10 B(n,α) 7 Li capture reaction. The theory of BNCT has been around for a long time since 1936, but has historically been limited by poor boron delivery agents and non-optimal neutron source facilities. Although significant improvements have been made in both of these domains, it is mainly the advancements of accelerator-based neutron sources that have led to the expansion of over 20 new BNCT facilities worldwide in the past decade. Additionally in this work, particle and heavy ion transport code system simulations, in collaboration with the University of Tsukuba, were performed to examine the effectiveness of the Ibaraki BNCT beam shaping assembly to moderate a neutron beam suitable for BNCT at the proposed prototype Canadian compact accelerator-based neutron source (CANS) site, which uses a similar but slightly higher energy 10 MeV proton accelerator with a 1 mA average current. The advancements of CANSs in recent decades have enabled significant improvements in BNCT technologies, allowing it to become a more viable clinical treatment option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it