Blast Injuries: From Improvised Explosive Device Blasts to the Boston Marathon Bombing
Bibliographic record
Abstract
Although most trauma centers have experience with the imaging and management of gunshot wounds, in most regions blast wounds such as the ones encountered in terrorist attacks with the use of improvised explosive devices (IEDs) are infrequently encountered outside the battlefield. As global terrorism becomes a greater concern, it is important that radiologists, particularly those working in urban trauma centers, be aware of the mechanisms of injury and the spectrum of primary, secondary, tertiary, and quaternary blast injury patterns. Primary blast injuries are caused by barotrauma from the initial increased pressure of the explosive detonation and the rarefaction of the atmosphere immediately afterward. Secondary blast injuries are caused by debris carried by the blast wind and most often result in penetrating trauma from small shrapnel. Tertiary blast injuries are caused by the physical displacement of the victim and the wide variety of blunt or penetrating trauma sustained as a result of the patient impacting immovable objects such as surrounding cars, walls, or fences. Quaternary blast injuries include all other injuries, such as burns, crush injuries, and inhalational injuries. Radiography is considered the initial imaging modality for assessment of shrapnel and fractures. Computed tomography is the optimal test to assess penetrating chest, abdominal, and head trauma. The mechanism of blast injuries and the imaging experience of the victims of the Boston Marathon bombing are detailed, as well as musculoskeletal, neurologic, gastrointestinal, and pulmonary injury patterns from blast injuries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".