Prehospital Lessons From the War in Ukraine: Damage Control Resuscitation and Surgery Experiences From Point of Injury to Role 2
Bibliographic record
Abstract
The ongoing war in Ukraine presents unique challenges to prehospital medical care for wounded combatants and civilians. The purpose of this article is to identify, describe, and address gaps in prehospital care, casualty evacuation, and medical evacuation throughout Ukraine to share lessons for other providers. Observations and experiences of medical personnel were collected and analyzed, focusing on pain management, antibiotic use, patient assessment, mass casualty triage, blood loss, hypothermia, transport immobilization, and clinical governance. Gaps identified include limited access to pain management, lack of antibiotic guidance, inadequate patient assessment and triage, access to damage control resuscitation and blood, challenged transport immobilization practices, and challenges with clinical governance for both local and foreign providers. Improved prehospital care and casualty and medical evacuation in Ukraine are required, through increased use of empiric pain management, focused antibiotic guidance, enhanced patient assessment and triage in the form of training, access to prehospital blood, and better transport immobilization practices. A robust and active lessons learned program, trauma data capture, and quality improvement process is needed to reduce preventable morbidity and mortality in the war zone. The recommendations presented in this article serve as a starting point for improvements in prehospital care in Ukraine with potential to change prehospital training for the NATO alliance and other organizations operating in similar areas of conflict. Graphical Abstract.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".