Prehospital Lessons From the War in Ukraine: Damage Control Resuscitation and Surgery Experiences From Point of Injury to Role 2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it