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Record W4411002209 · doi:10.1093/milmed/usaf217

A Call for Comprehensive Reform of Military Medical Planning of NATO and Its Allies Based on Lessons From the Ukraine War—Cultural Context and the Human Factor

2025· article· en· W4411002209 on OpenAlexaff
Lennart G. Bongartz, John Quinn, C. Fransen, Dimitry Kovtunenko, К. В. Гуменюк, D. Surkov, Al Giwa

Bibliographic record

VenueMilitary Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsSurrey Memorial Hospital
Fundersnot available
KeywordsMilitary medicineContext (archaeology)Political scienceNavyMilitary personnelFactor (programming language)Public administrationLawHistoryComputer scienceArchaeology

Abstract

fetched live from OpenAlex

The systematic targeting of medical infrastructure, personnel, and casualty evacuation routes by Russia in Ukraine challenges whether the North Atlantic Treaty Organization (NATO) can still rely on the Geneva Conventions for the protection of medical assets and personnel. This necessitates a thorough review of NATO Standardization Agreements (STANAGs) and doctrine used for Medical Planning for future Large Scale Combat Operations (LSCO). However, drawing lessons from the Ukrainian experience requires consideration of cultural context, medical evidence, and human factors. Ukraine's military medical system is burdened by its inherited Soviet-era doctrine, which was centralized and resistant to change. Initial reforms, aided by foreign assistance, aimed to modernize this system, emphasizing decentralized medical supply and improved training. However, the ongoing conflict has revealed the persistence of cultural issues, such as false reporting, lack of critical thinking, poor accountability, and resistance to change. Negative experiences can lead to abandonment of proven medical interventions, and without a comprehensive trauma registry, the effectiveness of the military medical system cannot be assessed with certainty. Frontline medical personnel often face high attrition and lack comprehensive command and control, further exacerbating challenges in delivering effective medical support. Despite these challenges to human factors, examples of innovative problem-solving exist. These solutions will be lost if not put into peer-reviewed doctrine or shared in a formal process. Previous NATO engagements assumed air-dominance and safe casualty evacuation and treatment across all echelons of care. The Ukrainian battlefield has shown that a near-peer adversary willing to systematically target medical units and the casualty evacuation system has catastrophic effects on military and civilian healthcare. Near-total reliance on ground-based evacuation platforms has forced Ukrainians to repurpose a large variety of nonstandard vehicles for casualty evacuation, as military ambulances were destroyed and most tracked/armored vehicles are prioritized for combat operations. Future NATO doctrine should emphasize mine-drone-resistant evacuation platforms with versatile (electronic) countermeasures. With the destruction of critical medical facilities, coupled with a high operational tempo and a massive influx of battlefield casualties, conventional triage models had to be abandoned, and casualties may remain in the prehospital setting for hours or even days. Point-of-injury stabilization has, therefore, taken on an even greater role than envisioned in NATO doctrine, and Ukrainian medical personnel adopted high-mobility approaches, such as delivery of lifesaving materials using drones. As medical treatment facilities came under attack, Ukrainians have resorted to distributed and hidden "micro-hospitals" and highly mobile surgical teams to avoid detection. The logistical challenges faced by Ukraine's medical system exposed weaknesses in NATO's approach to medical supply chains, requiring a more decentralized and on-demand medical supply model. The high level of civil-military medical coordination seen in Ukraine is far beyond what NATO doctrine currently envisions. Planning capabilities and tactical decision-making should be included in doctrine and in the curriculum of all medical unit leaders, with guiding frameworks that balance overall operational goals, personal safety, triage, and timely delivery of care. Co-development using validated "lessons learned" from Ukraine can provide a reliable roadmap for strategic reform.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.370
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2025
Admission routes1
Has abstractyes

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