Extravasation of Intravenous Computed Tomography Scan Contrast in Blunt Abdominal and Pelvic Trauma
Bibliographic record
Abstract
BACKGROUND: Intravenous contrast extravasation (CE) on computed tomography (CT) scan in blunt abdominal trauma is generally regarded as an indication for the need for invasive intervention (either angiography or laparotomy). More recently, improvements in CT scan technology have increased the sensitivity in detecting CE, and, thus, we postulate that not all patients with this finding require intervention. METHODS: This study is a retrospective review of all patients who underwent a CT scan for blunt abdominal trauma between January 1999 and September 2003. Patterns of injury, associated injuries, management, and outcomes were examined for patients with CE. RESULTS: Seventy of 1,435 patients (4.8%) demonstrated CE. Mean age was 44 years and mean Injury Severity Score was 39. The location of CE was intra-abdominal in 25, pelvis/retroperitoneum in 39, and both areas in 3 patients. Six patients received supportive treatment for nonsurvivable head injury and were excluded from further analysis. Overall, 30 (47%) patients underwent immediate intervention (angiography or laparotomy) and 34 (53%) were managed nonoperatively. Of those who had initial nonoperative management, overall seven (20.5%) underwent intervention, with the remainder being managed without intervention. The success for nonoperative management was greater for those with pelvic/retroperitoneal CE (4 of 7: 57%) than for intra-abdominal extravasation (23 of 27: 85%). CONCLUSION: Although evidence of CE may suggest significant vascular injury, our data suggest that not all patients require invasive intervention. Further studies are needed to better define criteria for nonoperative management in patients with CE identified on their initial CT scan.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".