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 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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".