Global Surgery 2030: a roadmap for high income country actors
Bibliographic record
Abstract
The Millennium Development Goals have ended and the Sustainable Development Goals have begun, marking a shift in the global health landscape. The frame of reference has changed from a focus on 8 development priorities to an expansive set of 17 interrelated goals intended to improve the well-being of all people. In this time of change, several groups, including the Lancet Commission on Global Surgery, have brought a critical problem to the fore: 5 billion people lack access to safe, affordable surgical and anaesthesia care when needed. The magnitude of this problem and the world's new focus on strengthening health systems mandate reimagined roles for and renewed commitments from high income country actors in global surgery. To discuss the way forward, on 6 May 2015, the Commission held its North American launch event in Boston, Massachusetts. Panels of experts outlined the current state of knowledge and agreed on the roles of surgical colleges and academic medical centres; trainees and training programmes; academia; global health funders; the biomedical devices industry, and news media and advocacy organisations in building sustainable, resilient surgical systems. This paper summarises these discussions and serves as a consensus statement providing practical advice to these groups. It traces a common policy agenda between major actors and provides a roadmap for maximising benefit to surgical patients worldwide. To close the access gap by 2030, individuals and organisations must work collectively, interprofessionally and globally. High income country actors must abandon colonial narratives and work alongside low and middle income country partners to build the surgical systems of the future.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.026 | 0.023 |
| Insufficient payload (model declined to judge) | 0.061 | 0.022 |
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".