Imaging of Traumatic Diaphragmatic Rupture: Evaluation of Diagnostic Accuracy at a Level 1 Trauma Centre
Bibliographic record
Abstract
PURPOSE: Traumatic diaphragmatic rupture (TDR) is an uncommon injury that can be associated with significant morbidity if not detected and treated in a timely manner. The purpose of our study was to evaluate the diagnostic accuracy of 64-slice multidetector computed tomography (64-MDCT) for the detection of TDR in patients at our level 1 trauma centre. METHODS: We used our hospital's trauma registry to identify patients with a diagnosis of TDR from January 1, 2008, to December 31, 2012. Only patients with a 64-MDCT scan at presentation who subsequently underwent laparotomy/laparoscopy were included in the study cohort. Using surgical findings as the gold standard, the accuracy of the prospective radiology reports was analyzed. RESULTS: Of the 3225 trauma patients who presented to our institution, 38 (1.2%) had a TDR. Fourteen of the 38 were excluded as they did not have MDCT before surgery. The study cohort consisted of 20 males and 4 females with a median age of 34.5 years and a median Injury Severity Score (ISS90) of 26. Fifteen had blunt trauma while 9 had a penetrating injury. The overall sensitivity of the radiology reports was 66.7% (95% confidence interval [CI]: 46.7%-82.0%), specificity was 100% (95% CI: 94.1%-100%), positive predictive value was 100% (95% CI: 80.6%-100%), negative predictive value was 88.4% (95% CI: 78.8%-94.0%), and accuracy was 90.6% (95% CI: 82.5%-95.2%). However, only 3 of 9 patients with penetrating injury had a correct preoperative diagnosis. Two of the 6 missed penetrating trauma cases had only indirect signs of injury. CONCLUSIONS: The detection of TDR in trauma patients on 64-MDCT can be improved, especially in patients presenting with penetrating injury. A careful search for subtle diaphragmatic defects and indirect evidence of injury is important to avoid missing the diagnosis.
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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 | 0.004 | 0.055 |
| 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".