Global Surgery at the National Landscape: Perspectives after the XXXIV Brazilian Congress of Surgery
Bibliographic record
Abstract
The XXXIV Brazilian Congress of Surgery included Global Surgery for the first time in its scientific program. Global Surgery is any action in research, clinical practice, and policy-making that aims to improve access and quality of care in surgical specialties. In 2015, The Lancet Commission on Global Surgery highlighted that five billion people lack safe, timely, and affordable surgical care. Even more critical, nine of ten people cannot access essential surgical care in low and middle-income countries, where a third of the worldwide population resides, and only 6% of global surgical procedures are performed. Although Brazilian researchers and institutions have been contributing to lay the movement's foundations since 2014, Global Surgery remains a barely debated subject in the country. It is urgent to expand the field and break paradigms regarding the surgeons' role in public health in Brazil. Accomplishing these standards requires a joint effort to strategically allocate resources and identify collaboration opportunities, including those from medical societies and regulatory bodies. As members of the International Student Surgical Network of Brazil - a nonprofit organization by and for students, residents, and young physicians focused on Global Surgery - we review why investing in surgery is cost-effective to strengthen health systems, reduce morbimortality, and lead to economic development. Additionally, we highlight and propose key recommendations to foster the field at the national level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".