Impact of High-Dose-Rate Brachytherapy Training via Telehealth in Low- and Middle-Income Countries
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Bibliographic record
Abstract
PURPOSE Our objective was to demonstrate the efficacy of a telehealth training course on high-dose-rate (HDR) brachytherapy for gynecologic cancer treatment for clinicians in low- and middle-income countries (LMICs) METHODS A 12-week course consisting of 16 live video sessions was offered to 10 cancer centers in the Middle East, Africa, and Nepal. A total of 46 participants joined the course, and 22 participants, on average, attended each session. Radiation oncologists and medical physicists from 11 US and international institutions prepared and provided lectures for each topic covered in the course. Confidence surveys of 15 practical competencies were administered to participants before and after the course. Competencies focused on HDR commissioning, shielding, treatment planning, radiobiology, and applicators. Pre- and post-program surveys of provider confidence, measured by 5-point Likert scale, were administered and compared. RESULTS Forty-six participants, including seven chief medical physicists, 16 senior medical physicists, five radiation oncologists, and three dosimetrists, representing nine countries attended education sessions. Reported confidence scores, both aggregate and paired, demonstrated increases in confidence in all 15 competencies. Post-curriculum score improvement was statistically significant ( P < .05) for paired respondents in 11 of 15 domains. Absolute improvements were largest for confidence in applicator commissioning (2.3 to 3.8, P = .009), treatment planning system commissioning (2.2 to 3.9, P = .0055), and commissioning an HDR machine (2.2 to 4.0, P = .0031). Overall confidence in providing HDR brachytherapy services safely and teaching other providers increased from 3.1 to 3.8 and 3.0 to 3.5, respectively. CONCLUSION A 12-week, low-cost telehealth training program on HDR brachytherapy improved confidence in treatment delivery and teaching for clinicians in 10 participating LMICs.
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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) | 0.001 | 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