A Competency-Based Ultrasound-Guided Breast Biopsy Training Program for Radiologists From Low-and-Middle-Income Countries that Leverages Mobile Health Technology (NCT04501419): A Study Protocol
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Bibliographic record
Abstract
IntroductionWhile ultrasound-guided breast biopsy (UGBB) performed by a radiologist is the standard of care in high-income countries for diagnosing breast cancer, blind or surgical biopsy has been the norm in low-and middle-income countries (LMIC) in part because LMIC radiologists lack the skill to perform UGBB. We present the study protocol of a competency-based UGBB training program for LMIC Nigerian radiologists that leverages mobile health technology.MethodsThis institutional review board-approved prospective multi-institutional single-arm clinical trial (ClinicalTrials.gov identifier: NCT04501419) involves 13 Nigerian radiologists from eight tertiary hospitals in South West and South East Nigeria. Our training program is unique because it uses a competency-based curriculum developed specifically for LMIC radiologists. The competency-based curriculum incorporates blended learning (e-learning and trainer-led), simulation (supervised and unsupervised), and patient biopsy (supervised and unsupervised) components. The study time frame is two years: 1 year for the trainees to complete active training and patient recruitment and another 1 year for patient follow-up. Primary outcome measures include trainees' competency (measured using the Ottawa Surgical Competency Operating Room Evaluation (O-SCORE)), the radiology-pathology concordance rate, and the complication rate. Secondary outcome measures include the diagnostic interval and the positive predictive value of UGBB.ConclusionBuilding capacity for UGBB in Nigeria and other LMIC can potentially improve breast cancer outcomes through early diagnosis. This training program is part of an implementation multi-component strategy package in Nigeria to improve breast cancer outcomes. This training program can also be adapted for other image-guided procedures that could impact global cancer control through diagnosis, therapeutic intervention, and/or palliation.
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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 | 0.001 | 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