AB017. Perceptions of Canadian radiation oncologists, medical physicists and radiation trainees about the feasibility and need of boron neutron capture therapy in Canada: a national survey
Bibliographic record
Abstract
Background: We are planning to develop accelerator-based boron neutron capture therapy (AB-BNCT). However, there is no clear understanding about how Canadian radiation oncologists (RO), medical physicists (MP), and their trainees perceive BNCT and its impact on radiation oncology as a discipline. The purpose of this study is to identify the challenges to build the first BNCT Center in Canada. Methods: This survey contains 17 questions in three domains: eligibility, demographics, and specific knowledge of BNCT. Eligibility is limited to RO with an independent/academic license, board-certified MP, or residents in a formal residency-training program. It is voluntary, anonymous, and without compensation. The results were analyzed using descriptive statistics. Results: We received 118 valid responses from all 10 provinces: 70 RO (59.3%) and 48 MP (40.7%), including seven RO and two MP residents. Age group, gender, and years of practice are well representing current workforce (e.g., 40.7% in age group 35–45 years, 72% male, and 30.5% had 10–20 years independent practice). Most know BNCT’s rationale (60.2%). Only 1.4% RO referred, observed, or participated in BNCT, vs. 0%, 2.1%, and 2.1% in MP, respectively. Many do not know the reasons of early BNCT’s failure (44.1%). Others blame lack of clinical trials and limited neutron sources (42.4%), nuclear reactors not suited to perform treatment (34.7%), no modern treatment planning system (34.7%), lack of precision in measuring boron concentration in vivo (28.8%), no effective boron compounds (24.6%), or presence of undesired radiation in neutron beam (16.9%). Only 5–29.7% correctly identified the current global BNCT developments and new International Atomic Energy Agency (IAEA) guideline being revised in 2020. BNCT was recommended for the four common indications by 15.7–18.6% of RO. The majority (87.3%) agreed that Canada should join BNCT research and 88.1% of RO/MP would refer an eligible cancer patient to a Canadian BNCT center when it becomes available. Conclusions: Most RO/MP support Canada to join BNCT global research. However, the limited knowledge that Canadian RO/MP have about current global BNCT practice and lack of experience remains a challenge. Further educational sessions to promote BNCT is necessary to realize this innovative cancer treatment in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".