Development of a Unifying Target and Consensus Indicators for Global Surgical Systems Strengthening: Proposed by the Global Alliance for Surgery, Obstetric, Trauma, and Anaesthesia Care (The G4 Alliance)
Bibliographic record
Abstract
After decades on the margins of primary health care, surgical and anaesthesia care is gaining increasing priority within the global development arena. The 2015 publications of the Disease Control Priorities third edition on Essential Surgery and the Lancet Commission on Global Surgery created a compelling evidenced-based argument for the fundamental role of surgery and anaesthesia within cost-effective health systems strengthening global strategy. The launch of the Global Alliance for Surgical, Obstetric, Trauma, and Anaesthesia Care in 2015 has further coordinated efforts to build priority for surgical care and anaesthesia. These combined efforts culminated in the approval of a World Health Assembly resolution recognizing the role of surgical care and anaesthesia as part of universal health coverage. Momentum gained from these milestones highlights the need to identify consensus goals, targets and indicators to guide policy implementation and track progress at the national level. Through an open consultative process that incorporated input from stakeholders from around the globe, a global target calling for safe surgical and anaesthesia care for 80% of the world by 2030 was proposed. In order to achieve this target, we also propose 15 consensus indicators that build on existing surgical systems metrics and expand the ability to prioritize surgical systems strengthening around the world.
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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 | 0.131 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".