CT of Penetrating Abdominopelvic Trauma
Bibliographic record
Abstract
Penetrating abdominopelvic trauma usually results from abdominal cavity violation from a firearm injury or a stab wound and is a leading cause of morbidity and mortality from traumatic injuries. Penetrating trauma can have subtle or complex imaging findings, posing a diagnostic challenge for radiologists. Contrast-enhanced CT is the modality of choice for evaluating penetrating injuries, with good sensitivity and specificity for solid-organ and hollow viscus injuries. Familiarity with the projectile kinetics of penetrating injuries is an important skill set for radiologists and aids in the diagnosis of both overt and subtle injuries. CT trajectography is a useful tool in CT interpretation that allows the identification of subtle injuries from the transfer of kinetic injury from the projectile to surrounding tissue. In CT trajectography, after the entry and exit wounds are delineated, the two points can be connected by placing cross-cursors and swiveling the cut planes obliquely in orthogonal planes to obtain a double-oblique orientation to visualize the wound track in profile. The path of the projectile and its ensuing damage is not always straight, and the imaging characteristics of free fluid of different attenuation in the abdomen (including hemoperitoneum) can support the diagnosis of visceral and vascular injuries. In addition, CT is increasingly used for evaluation of patients after damage control surgery and helps guide the management of injuries that were overlooked at surgery. An invited commentary by Paes and Munera is available online. Online supplemental material is available for this article. ©RSNA, 2021
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.039 | 0.005 |
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".