Ethiopia's first minimally invasive surgery program: A novel approach in global surgical education
Bibliographic record
Abstract
Objective Complex lung diseases are among the leading causes of death in Ethiopia. Access to thoracic surgery is limited, and before 2016 no thoracic surgeons were trained in minimally invasive surgery. A global academic partnership was formed between the University of Toronto and Addis Ababa University. We describe implementation of the first minimally invasive surgery training program in sub-Sahara Africa and evaluate its safety. Methods We performed a retrospective cohort analysis of open versus minimally invasive thoracic and upper gastrointestinal procedures performed at Addis Ababa University from January 2016 to June 2021. Baseline demographic, diagnostic, operative, and postoperative outcomes including length of stay and complications were compared. Results In our bilateral model of surgical education, training is provided in Ethiopia and Canada over 2 years with a focus on capacity building through egalitarian forms of knowledge exchange. Program features included certification in Fundamentals of Laparoscopic Surgery, high-fidelity lobectomy simulation, and hands-on training. Overall, 41 open and 56 minimally invasive surgery cases were included in the final statistical analysis. The average length of stay in the minimally invasive surgery group was 5.2 days versus 11.0 days in the open group ( P < .001). The overall complication rate was 18% in the minimally invasive surgery group versus 39% in the open group ( P = .020). Conclusions We demonstrated the successful initiation of sub-Sahara Africa's first minimally invasive surgery program in thoracic and upper gastrointestinal surgery and characterize its patient safety. We envision the minimally invasive surgery program as a template to continue expanding global partnerships and improving surgical care in other resource-limited 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".