Detection of significant bowel and mesenteric injuries in blunt abdominal trauma with 64-slice computed tomography
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Approximately 5% of blunt abdominal trauma patients experience blunt bowel and mesenteric injuries (BBMIs). The diagnosis may be elusive as computed tomography (CT) can occasionally miss these injuries. Recent advancements in CT technology, however, may improve detection rates. This study will assess the false-negative rate of BBMI using a 64-slice computed tomographic scanner in adults with blunt abdominal trauma. METHODS: All blunt abdominal trauma patients with laparotomy confirmed BBMI were retrospectively identified within a 5-year period at a Level I trauma center. Only patients who underwent preoperative abdominal CT were included. CT reports were examined specifically for findings suggestive of BBMI and compared with operative findings. A completely normal computed tomographic scan result as interpreted by a staff radiologist but operative findings of BBMI was considered a false negative. RESULTS: One hundred ninety five cases of laparotomy-proven BBMI were identified from the trauma registry, of which 68 patients met study inclusion criteria. All study patients had free fluid present on CT. As a result, there were no false-negative computed tomographic scan results for BBMI. Four patients had isolated small amounts of free fluid without any additional suggestive CT findings of BBMI or solid-organ injury. Mesenteric or bowel hematomas and bowel wall thickening were present in 57% and 50% of cases, respectively. CONCLUSION: The false-negative rates of BBMI may be reduced with a 64-slice computed tomographic scan. In this study, all patients had free fluid identified on CT. Consequently, even minimal free fluid remains relevant in patients with blunt abdominal injury. LEVEL OF EVIDENCE: Diagnostic test, level III.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it