Active extravasation of arterial contrast agent on post-traumatic abdominal computed tomography.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the use of emergent dynamic intravenous contrast-enhanced computed tomography (CT) in the diagnosis of active arterial extravasation in patients admitted to hospital after blunt abdominal trauma. METHODS: Four-hundred and ninety-eight consecutive emergent contrast-enhanced computed tomographic images of the abdomen and pelvis were retrospectively reviewed. The presence of and site(s) of active arterial extravasation were recorded. Two radiologists reviewed the images and compared the site(s) of extravasated arterial contrast agent with the site(s) of active hemorrhage established at angiography (n = 9) or surgery (n = 4). RESULTS: Twenty-eight patients' computed tomographic images were identified as showing signs of extravasation of contrast agent representing active arterial bleeding. A total of 49 sources of active arterial extravasation were identified, 37 in 19 patients. A pelvic source of active arterial hemorrhage was most frequent and was typically associated with unstable pelvic fractures (n = 18). Other sources of active arterial hemorrhage included the liver (n = 3), spleen (n = 2), retroperitoneum (n = 1), kidney (n = 1), mesentery (n = 1), abdominal wall (n = 3) and lumbar region (n = 1). Only 9 of 28 patients became sufficiently hemodynamically unstable to warrant angiography. All 9 patients had a pelvic source of arterial extravasation on contrast-enhanced CT, and 7 demonstrated active bleeding requiring embolization. The contrast-enhanced computed tomographic images correctly indicated the anatomical source of bleeding in all 7 cases. CONCLUSION: In patients who have experienced blunt abdominal trauma, attention should be paid to the computed tomographic features of active arterial hemorrhage. In our series, the pelvis was the most common source of active arterial bleeding, which was typically associated with unstable pelvic fractures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".