Unifying Children’s Surgery and Anesthesia Stakeholders Across Institutions and Clinical Disciplines: Challenges and Solutions from Uganda
Bibliographic record
Abstract
BACKGROUND: There is a significant unmet need for children's surgical care in low- and middle-income countries (LMICs). Multidisciplinary collaboration is required to advance the surgical and anesthesia care of children's surgical conditions such as congenital conditions, cancer and injuries. Nonetheless, there are limited examples of this process from LMICs. We describe the development and 3-year outcomes following a 2015 stakeholders' meeting in Uganda to catalyze multidisciplinary and multi-institutional collaboration. METHODS: The stakeholders' meeting was a daylong conference held in Kampala with local, regional and international collaborators in attendance. Multiple clinical specialties including surgical subspecialists, pediatric anesthesia, perioperative nursing, pediatric oncology and neonatology were represented. Key thematic areas including infrastructure, training and workforce retention, service delivery, and research and advocacy were addressed, and short-term objectives were agreed upon. We reported the 3-year outcomes following the meeting by thematic area. RESULTS: The Pediatric Surgical Foundation was developed following the meeting to formalize coordination between institutions. Through international collaborations, operating room capacity has increased. A pediatric general surgery fellowship has expanded at Mulago and Mbarara hospitals supplemented by an international fellowship in multiple disciplines. Coordinated outreach camps have continued to assist with training and service delivery in rural regional hospitals. CONCLUSION: Collaborations between disciplines, both within LMICs and with international partners, are required to advance children's surgery. The unification of stakeholders across clinical disciplines and institutional partnerships can facilitate increased children's surgical capacity. Such a process may prove useful in other LMICs with a wide range of children's surgery stakeholders.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".