Role of Imaging in Penetrating and Blunt Traumatic Injury to the Heart
Bibliographic record
Abstract
Cardiac injury due to blunt or penetrating chest trauma is common and is associated with significant morbidity and mortality. Understanding the mechanisms, types, and complications of cardiac injuries and the roles of various imaging modalities in characterizing them is important for appropriate diagnosis and treatment. These injuries have not been well documented at imaging, but there are now fast and accurate methods for evaluating the heart and associated mediastinal structures. The authors review the broad spectrum of injuries that can result from blunt or penetrating trauma to the chest, as well as the imaging modalities commonly used in the acute trauma setting for evaluation of the heart and mediastinal structures. A pictorial review of both common and, to date, rarely documented cardiac injuries imaged with a variety of modalities is also presented. While many imaging modalities are available, the authors demonstrate the value of multidetector computed tomography (CT) for the initial evaluation of patients with blunt or penetrating chest trauma. With the advent of multidetector CT, imaging of cardiac injury has increased and accurate identification of these rare but potentially lethal injuries has become paramount for improving survival. Selection of the most appropriate modality for evaluation and recognition of the imaging findings in cardiac injuries in the acute trauma setting is important to expedite treatment and improve survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".