Diagnosis and Outcome of Blunt Caval Injuries in the Modern Trauma Center
Bibliographic record
Abstract
BACKGROUND: Blunt vena caval injury (BCI) is uncommon with only a few published reports in the literature. Recently, with high resolution computed tomography (CT) scan imaging signs of caval injury are sometimes found in hemodynamically stable patients. The purpose of this study was to assess the current course of patients with BCI. METHODS: Retrospective review of all patients with BCI treated at a Regional Trauma Center from April 1999 to May 2005. Data collected included demographics, mechanism of injury, associated injuries, diagnostic investigations, surgical findings, and outcomes. RESULTS: During the 6-year study period, 10 patients presented with BCI (age 42 +/- 19 years; 70% mortality; Injury Severity Score 39 +/- 15). The spectrum of vena cava injury ranged from an intimal flap to extensive destruction. Six of the seven deaths were secondary to exsanguination and one secondary to severe brain injury. Four patients presented with refractory shock and were taken emergently to surgery (all died). Six patients responded to fluid resuscitation and underwent CT imaging (three out of six survived). Although active venous contrast extravasation was not seen in any patient, all six had indirect signs on CT suggestive of BCI. Overall, the diagnosis of BCI was confirmed at surgery in nine patients. The remaining patient had an intimal flap and contained pericaval hematoma confirmed by ultrasound, and was successfully managed nonoperatively. CONCLUSIONS: The spectrum of BCI ranges from intimal flaps to extensive destruction. CT imaging may not diagnose or may underestimate the severity of BCI. Stable patients with intimal flaps and contained hematoma may be successfully managed nonoperatively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".