Enhancing surgical capacity in the low- to middle-income countries
Bibliographic record
Abstract
INTRODUCTION: Pediatric and adult reconstructive urology remain underrepresented in global surgical efforts, despite their critical role in restoring genitourinary function. This global surgery initiative aimed to address the gap in specialized urologic care in low- to middle-income countries (LMICs) through a longitudinal, mentorship-based approach integrating augmented reality (AR) telementoring. METHODS: This report describes an approach used to enhance global surgical expertise in LMICs and summarizes data documenting impact. A Global Surgery Partnership Initiative was launched by an academic surgeon from the University of Toronto to address the lack of specialized pediatric and reconstructive urologic training. Through collaboration with local institutions in the Philippines and Vietnam, the program employed a mixed-method approach that delivered longitudinal mentorship, combining virtual case conferences, in-person surgical mentoring, pilot of AR-supported telementoring, and continuous postoperative coaching. Patient outcomes were assessed and mentees self-reported pre- and post-intervention surveys evaluating comfort and technical understanding. Descriptive statistics and paired t-tests were used to analyze outcomes. RESULTS: Thirty-eight pediatric and adult reconstructive urology cases were performed. Over time, operative times and length of stay decreased, with low complication rates (6/38, 12.7%) and Clavien-Dindo ≥3 complications (3/38, 8%). Mentee comfort and understanding significantly improved (mean comfort score: 3.06 to 6.77; technical understanding: 4.77 to 8.43; p<0.001). AR-assisted mentoring, introduced in 2022 and expanded in 2024, showed feasibility, with further enhanced intraoperative feedback and sustainability. CONCLUSIONS: This structured, mixed-method model effectively improved surgical competencies and system-level capacity in LMICs. Unlike short-term missions, this initiative emphasized continuity, adaptability, and sustainability. It presents a scalable framework for integrating reconstructive urology into global health programs while leveraging AR to overcome geographic and resource limitations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".