Strengthening Reconstructive Urology with an Aim for Capacity-Building in a Low-Middle-Income Country: A Multi-Institutional Global Surgery Collaboration Initial Report
Bibliographic record
Abstract
Background/Objectives: Reconstructive urology is critically underrepresented in global surgery initiatives, despite its essential role in managing congenital and acquired urogenital conditions. In response, a multinational Global Surgery Collaborative was launched in 2022 by a faculty from the University of Toronto, aiming to enhance reconstructive urology capacity in the Philippines, among other low- to low-middle-income countries through longitudinal mentorship and skills transfer. This report presents early experience from 2022 to 2024. Methods: This collaboration delivered annual in-person surgical missions from 2022 to 2024 at two major Philippine healthcare institutions. Training focused on pediatric and adult reconstructive urologic procedures. Local mentees participated in structured preoperative planning, intraoperative teaching, and postoperative debriefing. We conducted a prospective service evaluation comprising a prospective registry of consecutive cases and paired pre/post trainee surveys. Data were collected on patient demographics and surgical metrics. Primary clinical endpoints included operative time, length of stay, and complications (Clavien–Dindo), with standardized follow-up windows. Mentee educational outcomes were assessed through pre- and post-training trainee-reported (Likert) measures, evaluating comfort and technical understanding. Statistical analysis used the Wilcoxon signed-rank test to assess changes. Results: Over three years, 33 surgical cases were performed with 45 surgical resident mentees (Post-graduate year (PGY)4–PGY6) engaged. The median patient age was 23 (inter-quartile range [IQR] 12.5–41.5) years, with 33.3% pediatric and 84.8% of cases classified as major. The complication rate was 15.1%, with only one major event (3%). Across 45 mentees, comfort increased from a median 4.0 (IQR 2.5–5.0) to 7.0 (5.5–8.0) and technique understanding from 5.0 (4.0–6.5) to 9.0 (8.0–10.0), with large Wilcoxon effects (r = 0.877 and r = 0.875; both p < 0.001). Year-by-year analyses showed the same pattern with large effects. Conclusions: In this early three-year experience (33 cases, 84.8% major), this multi-institutional collaboration longitudinal mentorship model was feasible and safe, and was associated with significant trainee-reported improvements in comfort and technical understanding. This demonstrates a replicable model for global surgery in reconstructive urology, successfully enhancing surgical skills and fostering sustainable capacity in low- and middle-income countries (LMIC) settings.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.010 |
| 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".