Jejunum and ileum blunt trauma: what has changed with the implementation of multislice computed tomography?
Bibliographic record
Abstract
OBJECTIVE: to evaluate the impact of the new technology of multidetector computed tomography (MDCT) in improving the accuracy and early diagnosis of BSBI. METHODS: patients with blunt small bowel injuries (BSBI) grade> I were identified retrospectively and their CT scans reviewed by an experienced radiologist. Clinical and tomographic findings were analyzed and patients grouped as "pre-MDCT" and "post-MDCT", according to the time of implementation of a 64-slice MDCT. RESULTS: of the 26 patients with BSBI 16 had CT scans. Motor vehicle collision (62.5%) was the most frequent mechanism of injury. In the pre-MDCT period, five of the 13 patients (38.5%) had abdominal CT, and in the post-MDCT, 11 of 13 patients (84.6%) had the exam. During pre-MDCT, all CT scans were abnormal with findings of pneumoperitoneum (60%), free fluid (40%) and bowel wall enhancement (20%). In the post-MDCT group, all exams but one were abnormal and the most frequent findings were free fluid (90.9%), bowel wall enhancement (72.7%), and pneumoperitoneum (54.5%). However, the rate of delayed laparotomy did not change. The mortality rate in both groups were similar, with 20% during pre-MDCT and 18.2% during post-MDCT. CONCLUSION: the use of MDCT in abdominal trauma in our service has increased the sensibility of the diagnosis, but has had no impact on outcome so far.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".