A Consensus Framework for the Humanitarian Surgical Response to Armed Conflict in 21st Century Warfare
Bibliographic record
Abstract
Importance: Armed conflict in the 21st century poses new challenges to a humanitarian surgical response, including changing security requirements, access to patients, and communities in need, limited deployable surgical assets, resource constraints, and the requirement to address both traumatic injuries as well as emergency surgical needs of the population. At the same time, recent improvements in trauma care and systems have reduced injury-related mortality. This combination of new challenges and medical capabilities warrants reconsideration of long-standing humanitarian surgery protocols. Objective: To describe a consensus framework for surgical care designed to respond to this emerging need. Design, Setting, and Participants: An international group of 35 representatives from humanitarian agencies, US military, and academic trauma programs was invited to the Stanford Humanitarian Surgical Response in Conflict Working Group to engage in a structured process to review extant trauma protocols and make recommendations for revision. Main Outcomes and Measures: The working group's method adapted core elements of a modified Delphi process combined with consensus development conference from August 3 to August 5, 2018. Results: Lessons from civilian and military trauma systems as well as recent battlefield experiences in humanitarian settings were integrated into a tiered continuum of response from point of injury through rehabilitation. The framework addresses the security and medical requirements as well as ethical and legal principles that guide humanitarian action. The consensus framework includes trained, lay first responders; far-forward resuscitation/stabilization centers; rapid damage control surgical access; and definitive care facilities. The system also includes nontrauma surgical care, injury prevention, quality improvement, data collection, and predeployment training requirements. Conclusions and Relevance: Evidence suggests that modern trauma systems save lives. However, the requirements of providing this standard of care in insecure conflict settings places new burdens on humanitarian systems that must provide both emergency and trauma surgical care. This consensus framework integrates advances in trauma care and surgical systems in response to a changing security environment. It is possible to reduce disparities and improve the standard of care in these settings.
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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 | 0.334 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.015 | 0.014 |
| 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".