Detection of significant bowel and mesenteric injuries in blunt abdominal trauma with 64-slice computed tomography
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".