Analysis of the structure and nature of gunshot wounds of limbs during the Great Patriotic War and during modern local armed conflicts of the early 21st century
Bibliographic record
Abstract
The frequency of gunshot wounds to the extremities in the structure of combat surgical trauma in military conflicts of the 20th and early 21st centuries exceeds 60–70% and does not tend to decrease. Given the high intensity of combat operations in recent decades, the use of individual protective equipment by military personnel, and the widespread use of modern weapons of mass destruction, a comparative analysis of the structure and nature of gunshot wounds to the extremities during the Great Patriotic War and modern local conflicts in the first quarter of the 21st century is of particular interest. The review was based on an analysis of “Soviet Medicine’s Experience in the Great Patriotic War,” as well as domestic and foreign works that cover modern combat injuries to the extremities. It has been established that during the Great Patriotic War, the frequency of gunshot wounds to the extremities was 70.8%, while in the military conflicts of recent decades, their share in the structure of combat surgical trauma has increased to 71.7–81.4%. Soft tissue injuries during the Great Patriotic War accounted for 44.7% of all limb injuries. In modern combat operations, they account for 47–62%. The frequency of fractures of long tubular bones has increased from 45.4% during the Great Patriotic War to 53–60% in modern local military conflicts at the beginning of the 21st century. During the Great Patriotic War, injuries to the main vessels of the extremities occurred in 1% of cases, while in modern local military conflicts they are observed much more frequently (6–18%). During the Great Patriotic War, gunshot wounds to major arteries and veins of the extremities (including avulsions) accounted for 96.82% of vascular injuries of all locations. Of these, the upper extremities accounted for 38.84%, and the lower extremities for 57.98%. Due to the high number of gunshot avulsions to the extremities, arteriovenous lesions accounted for up to 64.5% of all blood vessel injuries, 45% of which occurred in the lower extremities. The proportion of nerve injuries to the extremities during the Great Patriotic War was 8–10%, comparable to figures from military conflicts in recent decades (10–12%). Simultaneous lesions of the nerve trunk and large blood vessels, which were observed in 5.4% of cases during the Great Patriotic War, have become more common in modern local military conflicts in the early 21st century. They have significantly increased and are observed in 10–25% of cases.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".