Global surgery and the sustainable development goals
Bibliographic record
Abstract
BACKGROUND: The field of global surgery has gained significant recent momentum, catalysed by the 2015 publication of the Lancet Commission on Global Surgery, Disease Control Priorities 3 and World Health Assembly resolution 68.15. These reports characterized the global burden of disease amenable to surgical care, called for global investment in surgical systems, and recognized surgery and anaesthesia as essential components of universal health coverage. METHODS: A strategy proposed to strengthen surgical care is the development of national surgical, obstetric and anaesthesia plans (NSOAPs). This review examined how NSOAPs could contribute to the achievement of sustainable development goals (SDGs) 1, 3, 5, 8, 9, 10, 16 and 17 by 2030, focusing on their potential impact on the healthcare systems in Ethiopia, Tanzania and Zambia. RESULTS: Due to the cross-cutting nature of surgery, obstetrics and anaesthesia, investing in these services will escalate progress to achieve gender equality, economic growth and infrastructure development. Universal health coverage will not be achieved without addressing the financial ramifications to the poor of seeking and receiving surgical care. NSOAPs provide a strategic framework and a data collection platform for evidence-based policy-making, accountability and implementation guidance. CONCLUSION: The development and implementation of data-driven NSOAPs should be recognized as a powerful road map to accelerate achievement of the SDGs by 2030.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".