Developing Global Gastrointestinal Care Capacity
Bibliographic record
Abstract
GOALS: Describe the establishment and expansion of a global gastrointestinal (GI) partnership and its impact on the advancement of GI care in Rwanda. BACKGROUND: GI disease disproportionately affects low- and middle-income countries. In Rwanda, governmental and nonprofit efforts have made significant progress in expanding access to health care in the wake of the 1994 genocide; however, specialized care remains largely unrecognized or inaccessible. In response, the Rwanda Ministry of Health established a partnership with an international gastroenterology NGO to address this critical need. STUDY: We present summative data generated by this partnership. To inform future global GI programs, we provide lessons learned and details about Rwanda's successful establishment of an in-country, self-sustainable, and locally governed gastroenterology training program. RESULTS: From 2017 to 2023, the partnership expanded the number of clinical sites receiving care from 4 to 11. Total procedural volume increased from 244 to 1069. Most common presenting upper GI symptoms included dyspepsia, reflux, and emesis, while most common lower GI symptoms included hematochezia and constipation. Since 2017, this partnership has increased the number of faculty volunteering in Rwanda (and Rwandan clinicians studying in US academic medical centers), expanded its geographical reach, assisted in the creation of a GI fellowship, and enabled Rwanda to become an East African hub for medical education. CONCLUSIONS: Lessons learned across ethical, financial, leadership, cultural, and medical domains from the successful establishment and expansion of an international GI partnership in Rwanda provides invaluable insights to guide development of future models of health care in emerging economies.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".